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import argparse
import logging
import os
import re
import gradio.processing_utils as gr_pu
import gradio as gr
import librosa
import numpy as np
import soundfile
from scipy.io import wavfile
import tempfile
import edge_tts
import utils
from inference.infer_tool import Svc
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)
sampling_rate = 44100
tts_voice = {
"中文男": "zh-CN-YunxiNeural",
"中文女": "zh-CN-XiaoyiNeural",
"英文男": "en-US-EricNeural",
"英文女": "en-US-AnaNeural"
}
hubert_dict = {
"vec768l12": utils.get_speech_encoder("vec768l12", device="cpu"),
"vec256l9": utils.get_speech_encoder("vec256l9", device="cpu")
}
def create_fn(model, spk):
def svc_fn(input_audio, vc_transform, auto_f0, f0p):
if input_audio is None:
return 0, None
sr, audio = input_audio
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
temp_path = "temp.wav"
soundfile.write(temp_path, audio, sampling_rate, format="wav")
model.hubert_model = hubert_dict[model.speech_encoder]
out_audio = model.slice_inference(raw_audio_path=temp_path,
spk=spk,
slice_db=-40,
cluster_infer_ratio=0,
noice_scale=0.4,
clip_seconds=10,
tran=vc_transform,
f0_predictor=f0p,
auto_predict_f0=auto_f0)
model.clear_empty()
os.remove(temp_path)
return sampling_rate, out_audio
async def tts_fn(input_text, gender, tts_rate, vc_transform, auto_f0, f0p):
if input_text == '':
return 0, None
input_text = re.sub(r"[\n\,\(\) ]", "", input_text)
voice = tts_voice[gender]
ratestr = "+{:.0%}".format(tts_rate) if tts_rate >= 0 else "{:.0%}".format(tts_rate)
communicate = edge_tts.Communicate(text=input_text, voice=voice, rate=ratestr)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
temp_path = tmp_file.name
await communicate.save(temp_path)
audio, sr = librosa.load(temp_path)
audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate)
os.remove(temp_path)
temp_path = "temp.wav"
wavfile.write(temp_path, sampling_rate, (audio * np.iinfo(np.int16).max).astype(np.int16))
sr, audio = gr_pu.audio_from_file(temp_path)
input_audio = (sampling_rate, audio)
return svc_fn(input_audio, vc_transform, auto_f0, f0p)
return svc_fn, tts_fn
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--api', action="store_true", default=False)
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
models = []
for f in os.listdir("models"):
name = f
model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config_{f}.json", device=args.device)
cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else f"models/{f}/cover.jpg"
models.append((name, cover, create_fn(model, name)))
with gr.Blocks() as app:
gr.Markdown(
"# <center> 罪恶都市角色语音生成\n"
"## <center> 模型作者：B站[Cyber蝈蝈总](https://space.bilibili.com/37706580)\n"
"#### <center> 圣安地列斯人物AI语音请移步[GTASA](https://huggingface.co/spaces/GroveStreet/GTA_SOVITS)，使用此资源创作的作品请标明出处\n"
)
with gr.Tabs():
for (name, cover, (svc_fn, tts_fn)) in models:
with gr.TabItem(name):
with gr.Row():
with gr.Column():
with gr.Row():
vc_transform = gr.Number(label="音高调整 (正负半音，12为1个八度)", value=0)
f0_predictor = gr.Radio(label="f0预测器 (推荐rmvpe)",
choices=['crepe', 'harvest', 'rmvpe'], value='rmvpe')
auto_f0 = gr.Checkbox(label="自动音高预测 (文本转语音或讲话可选,会导致唱歌跑调)",
value=False)
with gr.Tabs():
with gr.TabItem('语音转语音'):
svc_input = gr.Audio(
label="上传干声 (已支持无限长音频，处理时间约为原音频时间的5倍)")
svc_submit = gr.Button("生成", variant="primary")
with gr.TabItem('文本转语音'):
tts_input = gr.Textbox(label='说话内容', value='',
placeholder='已支持无限长内容，处理时间约为说完原内容时间的5倍')
with gr.Row():
gender = gr.Radio(label='说话人性别 (男音调低，女音调高)', value='中文男',
choices=['中文男', '中文女', '英文男', '英文女'])
tts_rate = gr.Number(label='语速 (正负, 单位百分比)', value=0)
tts_submit = gr.Button("生成", variant="primary")
with gr.Column():
gr.Markdown(
'<div align="center">'
f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
'</div>'
)
vc_output = gr.Audio(label="输出音频")
svc_submit.click(svc_fn, [svc_input, vc_transform, auto_f0, f0_predictor], vc_output)
tts_submit.click(tts_fn, [tts_input, gender, tts_rate, vc_transform, auto_f0, f0_predictor],
vc_output)
app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
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