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VC_inference.py
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VC_inference.py
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import os
import numpy as np
import torch
from torch import no_grad, LongTensor
import argparse
import commons
from mel_processing import spectrogram_torch
import utils
from models import SynthesizerTrn
import gradio as gr
import librosa
import webbrowser
from text import text_to_sequence, _clean_text
device = "cuda:0" if torch.cuda.is_available() else "cpu"
import logging
logging.getLogger("PIL").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("httpx").setLevel(logging.WARNING)
logging.getLogger("asyncio").setLevel(logging.WARNING)
language_marks = {
"Japanese": "",
"日本語": "[JA]",
"简体中文": "[ZH]",
"English": "[EN]",
"Mix": "",
}
lang = ['日本語', '简体中文', 'English', 'Mix']
def get_text(text, hps, is_symbol):
text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = LongTensor(text_norm)
return text_norm
def create_tts_fn(model, hps, speaker_ids):
def tts_fn(text, speaker, language, speed):
if language is not None:
text = language_marks[language] + text + language_marks[language]
speaker_id = speaker_ids[speaker]
stn_tst = get_text(text, hps, False)
with no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
sid = LongTensor([speaker_id]).to(device)
audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
del stn_tst, x_tst, x_tst_lengths, sid
return "Success", (hps.data.sampling_rate, audio)
return tts_fn
def create_vc_fn(model, hps, speaker_ids):
def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
input_audio = record_audio if record_audio is not None else upload_audio
if input_audio is None:
return "You need to record or upload an audio", None
sampling_rate, audio = input_audio
original_speaker_id = speaker_ids[original_speaker]
target_speaker_id = speaker_ids[target_speaker]
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != hps.data.sampling_rate:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
with no_grad():
y = torch.FloatTensor(audio)
y = y / max(-y.min(), y.max()) / 0.99
y = y.to(device)
y = y.unsqueeze(0)
spec = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
center=False).to(device)
spec_lengths = LongTensor([spec.size(-1)]).to(device)
sid_src = LongTensor([original_speaker_id]).to(device)
sid_tgt = LongTensor([target_speaker_id]).to(device)
audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
0, 0].data.cpu().float().numpy()
del y, spec, spec_lengths, sid_src, sid_tgt
return "Success", (hps.data.sampling_rate, audio)
return vc_fn
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file")
parser.add_argument("--share", default=False, help="make link public (used in colab)")
args = parser.parse_args()
hps = utils.get_hparams_from_file(args.config_dir)
net_g = SynthesizerTrn(
len(hps.symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(args.model_dir, net_g, None)
speaker_ids = hps.speakers
speakers = list(hps.speakers.keys())
tts_fn = create_tts_fn(net_g, hps, speaker_ids)
vc_fn = create_vc_fn(net_g, hps, speaker_ids)
app = gr.Blocks()
with app:
with gr.Tab("Text-to-Speech"):
with gr.Row():
with gr.Column():
textbox = gr.TextArea(label="Text",
placeholder="Type your sentence here",
value="こんにちわ。", elem_id=f"tts-input")
# select character
char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
label='速度 Speed')
with gr.Column():
text_output = gr.Textbox(label="Message")
audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
btn = gr.Button("Generate!")
btn.click(tts_fn,
inputs=[textbox, char_dropdown, language_dropdown, duration_slider,],
outputs=[text_output, audio_output])
with gr.Tab("Voice Conversion"):
gr.Markdown("""
录制或上传声音，并选择要转换的音色。
""")
with gr.Column():
record_audio = gr.Audio(label="record your voice", source="microphone")
upload_audio = gr.Audio(label="or upload audio here", source="upload")
source_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="source speaker")
target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
with gr.Column():
message_box = gr.Textbox(label="Message")
converted_audio = gr.Audio(label='converted audio')
btn = gr.Button("Convert!")
btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
outputs=[message_box, converted_audio])
webbrowser.open("http://127.0.0.1:7860")
app.launch(share=args.share)