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https://github.com/RVC-Boss/GPT-SoVITS.git
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推理部分，增加gradio的wav音频流式输出与TTS API
This commit is contained in:
Hao Zhou
parent
939971afe3
commit
a8d59e72f6
370
GPT_SoVITS/inference_stream.py
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370
GPT_SoVITS/inference_stream.py
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@ -0,0 +1,370 @@
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import os
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import tempfile, io, wave
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import gradio as gr
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import uvicorn
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import argparse
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from fastapi import FastAPI
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from fastapi.responses import StreamingResponse
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from pydub import AudioSegment
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from tools.i18n.i18n import I18nAuto
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from GPT_SoVITS.inference_webui import (
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get_weights_names,
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custom_sort_key,
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change_choices,
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change_gpt_weights,
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change_sovits_weights,
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get_tts_wav,
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)
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api_app = FastAPI()
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i18n = I18nAuto()
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# API mode Usage: python GPT_SoVITS/inference_stream.py --api
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parser = argparse.ArgumentParser(description="GPT-SoVITS Streaming API")
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parser.add_argument(
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"-api",
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"--api",
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action="store_true",
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default=False,
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help="是否开启API模式(不开启则是WebUI模式)",
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)
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parser.add_argument(
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"-s",
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"--sovits_path",
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type=str,
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default="GPT_SoVITS/pretrained_models/s2G488k.pth",
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help="SoVITS模型路径",
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)
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parser.add_argument(
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"-g",
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"--gpt_path",
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type=str,
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default="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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help="GPT模型路径",
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)
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parser.add_argument(
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"-rw",
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"--ref_wav",
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type=str,
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# default="./example/archive_ruanmei_8.wav",
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help="参考音频路径",
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)
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parser.add_argument(
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"-rt",
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"--prompt_text",
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type=str,
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# default="我听不惯现代乐，听戏却极易入迷，琴弦拨动，时间便流往过去。",
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help="参考音频文本",
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)
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parser.add_argument(
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"-rl",
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"--prompt_language",
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type=str,
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default="中文",
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help="参考音频语种",
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)
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args = parser.parse_args()
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sovits_path = args.sovits_path
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gpt_path = args.gpt_path
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SoVITS_names, GPT_names = get_weights_names()
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EXAMPLES = [
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[
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"中文",
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"根据过年的传说，远古时代有一隻凶残年兽，每到岁末就会从海底跑出来吃人。"
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+ "人们为了不被年兽吃掉，家家户户都会祭拜祖先祈求平安，也会聚在一起吃一顿丰盛的晚餐。"
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+ "后来人们发现年兽害怕红色、噪音与火光，便开始在当天穿上红衣、门上贴上红纸、燃烧爆竹声，藉此把年兽赶走。"
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+ "而这些之后也成为过年吃团圆饭、穿红衣、放鞭炮、贴春联的过年习俗。",
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],
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[
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"中文",
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"神霄折戟录其二"
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+"「嗯，好吃。」被附体的未央变得温柔了许多，也冷淡了很多。"
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+ "她拿起弥耳做的馅饼，小口小口吃了起来。第一口被烫到了,还很可爱地吐着舌头吸气。"
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+ "「我一下子有点接受不了, 需要消化消化。」用一只眼睛作为代价维持降灵的弥耳自己也拿了一个馅饼，「你再说一 遍?」"
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+ "「当年所谓的陨铁其实是神戟。它被凡人折断，铸成魔剑九柄。这一把是雾海魔剑。 加上他们之前已经收集了两柄。「然后你是?」"
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+ "「我是曾经的天帝之女，名字已经忘了。我司掌审判与断罪，用你们的话说，就是刑律。」"
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+ "因为光禄寺执掌祭祀典礼的事情，所以仪式、祝词什么的，弥耳被老爹逼得倒是能倒背如流。同时因为尽是接触怪力乱神，弥耳也是知道一些小]道的。 神明要是被知道了真正的秘密名讳，就只能任人驱使了。眼前这位未必是忘了。"
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+ "「所以朝廷是想重铸神霄之戟吗?」弥耳说服自己接受了这个设定，追问道。"
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+ "「我不知道。这具身体的主人并不知道别的事。她只是很愤怒,想要证明自己。」未央把手放在了胸口上。"
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+"「那接下来,我是应该弄个什么送神仪式把你送走吗?」弥耳摸了摸绷带下已经失去功能的眼睛，「然后我的眼睛也会回来?」"
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],
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]
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# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py
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def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000):
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# This will create a wave header then append the frame input
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# It should be first on a streaming wav file
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# Other frames better should not have it (else you will hear some artifacts each chunk start)
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wav_buf = io.BytesIO()
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with wave.open(wav_buf, "wb") as vfout:
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vfout.setnchannels(channels)
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vfout.setsampwidth(sample_width)
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vfout.setframerate(sample_rate)
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vfout.writeframes(frame_input)
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wav_buf.seek(0)
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return wav_buf.read()
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def get_streaming_tts_wav(
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ref_wav_path,
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prompt_text,
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prompt_language,
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text,
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text_language,
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how_to_cut,
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top_k,
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top_p,
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temperature,
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ref_free,
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byte_stream=True,
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):
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chunks = get_tts_wav(
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ref_wav_path=ref_wav_path,
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prompt_text=prompt_text,
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prompt_language=prompt_language,
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text=text,
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text_language=text_language,
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how_to_cut=how_to_cut,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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ref_free=ref_free,
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stream=True,
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)
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if byte_stream:
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yield wave_header_chunk()
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for chunk in chunks:
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yield chunk
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else:
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# Send chunk files
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i = 0
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format = "wav"
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for chunk in chunks:
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i += 1
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file = f"{tempfile.gettempdir()}/{i}.{format}"
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segment = AudioSegment(chunk, frame_rate=32000, sample_width=2, channels=1)
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segment.export(file, format=format)
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yield file
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def webui():
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with gr.Blocks(title="GPT-SoVITS Streaming Demo") as app:
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gr.Markdown(
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value=i18n(
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"流式输出演示，分句推理后推送到组件中。由于目前bytes模式的限制，采用<a href='https://github.com/gradio-app/gradio/blob/gradio%404.17.0/demo/stream_audio_out/run.py'>stream_audio_out</a>中临时文件的方案输出分句。这种方式相比bytes，会增加wav文件解析的延迟。"
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),
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)
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gr.Markdown(value=i18n("模型切换"))
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with gr.Row():
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GPT_dropdown = gr.Dropdown(
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label=i18n("GPT模型列表"),
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choices=sorted(GPT_names, key=custom_sort_key),
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value=gpt_path,
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interactive=True,
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)
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SoVITS_dropdown = gr.Dropdown(
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label=i18n("SoVITS模型列表"),
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choices=sorted(SoVITS_names, key=custom_sort_key),
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value=sovits_path,
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interactive=True,
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)
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refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
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refresh_button.click(
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fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]
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)
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SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
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GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
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gr.Markdown(value=i18n("*请上传并填写参考信息"))
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with gr.Row():
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inp_ref = gr.Audio(
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label=i18n("请上传3~10秒内参考音频，超过会报错！"), value=args.ref_wav, type="filepath"
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)
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with gr.Column():
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ref_text_free = gr.Checkbox(
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label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"),
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value=False,
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interactive=True,
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show_label=True,
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)
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gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT"))
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prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value=args.prompt_text)
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prompt_language = gr.Dropdown(
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label=i18n("参考音频的语种"),
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choices=[
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i18n("中文"),
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i18n("英文"),
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i18n("日文"),
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i18n("中英混合"),
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i18n("日英混合"),
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i18n("多语种混合"),
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],
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value=args.prompt_language,
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)
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def load_text(file):
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with open(file.name, "r", encoding="utf-8") as file:
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return file.read()
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load_button = gr.UploadButton(i18n("加载参考文本"), variant="secondary")
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load_button.upload(load_text, load_button, prompt_text)
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gr.Markdown(
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value=i18n(
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"*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"
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)
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)
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with gr.Row():
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text = gr.Textbox(
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label=i18n("需要合成的文本"), value="", lines=5, interactive=True
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)
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"),
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choices=[
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i18n("中文"),
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i18n("英文"),
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i18n("日文"),
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i18n("中英混合"),
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i18n("日英混合"),
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i18n("多语种混合"),
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],
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value=i18n("中文"),
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)
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how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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choices=[
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i18n("不切"),
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i18n("凑四句一切"),
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i18n("凑50字一切"),
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i18n("按中文句号。切"),
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i18n("按英文句号.切"),
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i18n("按标点符号切"),
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],
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value=i18n("按标点符号切"),
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interactive=True,
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)
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gr.Markdown(value=i18n("* 参数设置"))
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with gr.Row():
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with gr.Column():
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top_k = gr.Slider(
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minimum=1,
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maximum=100,
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step=1,
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label=i18n("top_k"),
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value=5,
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interactive=True,
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)
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top_p = gr.Slider(
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minimum=0,
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maximum=1,
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step=0.05,
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label=i18n("top_p"),
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value=1,
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interactive=True,
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)
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temperature = gr.Slider(
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minimum=0,
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maximum=1,
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step=0.05,
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label=i18n("temperature"),
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value=1,
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interactive=True,
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)
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inference_button = gr.Button(i18n("合成语音"), variant="primary")
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gr.Markdown(value=i18n("* 结果输出(等待第2句推理结束后会自动播放)"))
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with gr.Row():
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audio_file = gr.Audio(
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value=None,
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label=i18n("输出的语音"),
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streaming=True,
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autoplay=True,
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interactive=False,
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show_label=True,
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)
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inference_button.click(
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get_streaming_tts_wav,
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[
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inp_ref,
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prompt_text,
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prompt_language,
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text,
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text_language,
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how_to_cut,
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top_k,
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top_p,
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temperature,
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ref_text_free,
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],
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[audio_file],
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).then(lambda: gr.update(interactive=True), None, [text], queue=False)
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with gr.Row():
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gr.Examples(
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EXAMPLES,
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[text_language, text],
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cache_examples=False,
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run_on_click=False, # Will not work , user should submit it
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)
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app.queue().launch(
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server_name="0.0.0.0",
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inbrowser=True,
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share=False,
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server_port=8080,
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quiet=True,
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)
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@api_app.get("/")
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async def tts(
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text: str, # 必选参数
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language: str = i18n("中文"),
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top_k: int = 5,
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top_p: float = 1,
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temperature: float = 1,
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):
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ref_wav_path = args.ref_wav
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prompt_text = args.prompt_text
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prompt_language = args.prompt_language
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how_to_cut = i18n("按标点符号切")
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return StreamingResponse(
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get_streaming_tts_wav(
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ref_wav_path=ref_wav_path,
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prompt_text=prompt_text,
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prompt_language=prompt_language,
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text=text,
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text_language=language,
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how_to_cut=how_to_cut,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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ref_free=False,
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byte_stream=True,
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),
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media_type="audio/x-wav",
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)
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def api():
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uvicorn.run(
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app="inference_stream:api_app", host="127.0.0.1", port=8080, reload=True
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)
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if __name__ == "__main__":
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# 模式选择，默认是webui模式
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if not args.api:
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webui()
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else:
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api()
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@ -306,7 +306,7 @@ def merge_short_text_in_array(texts, threshold):
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result[len(result) - 1] += text
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return result
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free=False, stream=False):
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if prompt_text is None or len(prompt_text) == 0:
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ref_free = True
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t0 = ttime()
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@ -364,68 +364,81 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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texts = merge_short_text_in_array(texts, 5)
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audio_opt = []
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if not ref_free:
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phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
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phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
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else:
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phones1, bert1 = None, None
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for text in texts:
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# 解决输入目标文本的空行导致报错的问题
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if (len(text.strip()) == 0):
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continue
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if (text[-1] not in splits): text += "。" if text_language != "en" else "."
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print(i18n("实际输入的目标文本(每句):"), text)
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phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
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print(i18n("前端处理后的文本(每句):"), norm_text2)
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if not ref_free:
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
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else:
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bert = bert2
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all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
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||||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||||
t2 = ttime()
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_len,
|
||||
None if ref_free else prompt,
|
||||
bert,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
early_stop_num=hz * max_sec,
|
||||
)
|
||||
t3 = ttime()
|
||||
# print(pred_semantic.shape,idx)
|
||||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(
|
||||
0
|
||||
) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||||
if is_half == True:
|
||||
refer = refer.half().to(device)
|
||||
else:
|
||||
refer = refer.to(device)
|
||||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||||
audio = (
|
||||
vq_model.decode(
|
||||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
|
||||
)
|
||||
.detach()
|
||||
.cpu()
|
||||
.numpy()[0, 0]
|
||||
) ###试试重建不带上prompt部分
|
||||
max_audio=np.abs(audio).max()#简单防止16bit爆音
|
||||
if max_audio>1:audio/=max_audio
|
||||
audio = get_tts_chunk(ref_wav_path, text, text_language, bert1, phones1, prompt_semantic,
|
||||
top_k, top_p, temperature, ref_free, t0, t1)
|
||||
audio_opt.append(audio)
|
||||
audio_opt.append(zero_wav)
|
||||
t4 = ttime()
|
||||
if (stream):
|
||||
# 流式模式下每句返回一次
|
||||
yield (np.concatenate([audio, zero_wav], 0) * 32768).astype(np.int16).tobytes()
|
||||
|
||||
if (not stream):
|
||||
# 非流式最终合并后返回
|
||||
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
|
||||
np.int16
|
||||
)
|
||||
|
||||
def get_tts_chunk(ref_wav_path, text, text_language, bert1, phones1, prompt_semantic, top_k, top_p, temperature, ref_free, t0, t1):
|
||||
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
|
||||
print(i18n("实际输入的目标文本(每句):"), text)
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
|
||||
print(i18n("前端处理后的文本(每句):"), norm_text2)
|
||||
if not ref_free:
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
|
||||
else:
|
||||
bert = bert2
|
||||
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||||
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||||
t2 = ttime()
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_len,
|
||||
None if ref_free else prompt,
|
||||
bert,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
early_stop_num=hz * max_sec,
|
||||
)
|
||||
t3 = ttime()
|
||||
# print(pred_semantic.shape,idx)
|
||||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(
|
||||
0
|
||||
) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||||
if is_half == True:
|
||||
refer = refer.half().to(device)
|
||||
else:
|
||||
refer = refer.to(device)
|
||||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||||
audio = (
|
||||
vq_model.decode(
|
||||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
|
||||
)
|
||||
.detach()
|
||||
.cpu()
|
||||
.numpy()[0, 0]
|
||||
) ###试试重建不带上prompt部分
|
||||
max_audio=np.abs(audio).max()#简单防止16bit爆音
|
||||
if max_audio>1:audio/=max_audio
|
||||
t4 = ttime()
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
|
||||
np.int16
|
||||
)
|
||||
return audio
|
||||
|
||||
|
||||
def split(todo_text):
|
||||
@ -543,75 +556,79 @@ def get_weights_names():
|
||||
|
||||
SoVITS_names, GPT_names = get_weights_names()
|
||||
|
||||
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
gr.Markdown(
|
||||
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
|
||||
)
|
||||
with gr.Group():
|
||||
gr.Markdown(value=i18n("模型切换"))
|
||||
with gr.Row():
|
||||
GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
|
||||
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
|
||||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
|
||||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||||
SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
|
||||
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
|
||||
gr.Markdown(value=i18n("*请上传并填写参考信息"))
|
||||
with gr.Row():
|
||||
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
|
||||
with gr.Column():
|
||||
ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
|
||||
gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
|
||||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
|
||||
prompt_language = gr.Dropdown(
|
||||
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
|
||||
)
|
||||
gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
|
||||
with gr.Row():
|
||||
text = gr.Textbox(label=i18n("需要合成的文本"), value="")
|
||||
text_language = gr.Dropdown(
|
||||
label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
|
||||
)
|
||||
how_to_cut = gr.Radio(
|
||||
label=i18n("怎么切"),
|
||||
choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
|
||||
value=i18n("凑四句一切"),
|
||||
interactive=True,
|
||||
)
|
||||
def main():
|
||||
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
gr.Markdown(
|
||||
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
|
||||
)
|
||||
with gr.Group():
|
||||
gr.Markdown(value=i18n("模型切换"))
|
||||
with gr.Row():
|
||||
gr.Markdown("gpt采样参数(无参考文本时不要太低)：")
|
||||
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
|
||||
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
|
||||
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
|
||||
inference_button = gr.Button(i18n("合成语音"), variant="primary")
|
||||
output = gr.Audio(label=i18n("输出的语音"))
|
||||
GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
|
||||
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
|
||||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
|
||||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||||
SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
|
||||
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
|
||||
gr.Markdown(value=i18n("*请上传并填写参考信息"))
|
||||
with gr.Row():
|
||||
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
|
||||
with gr.Column():
|
||||
ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
|
||||
gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
|
||||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
|
||||
prompt_language = gr.Dropdown(
|
||||
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
|
||||
)
|
||||
gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
|
||||
with gr.Row():
|
||||
text = gr.Textbox(label=i18n("需要合成的文本"), value="")
|
||||
text_language = gr.Dropdown(
|
||||
label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
|
||||
)
|
||||
how_to_cut = gr.Radio(
|
||||
label=i18n("怎么切"),
|
||||
choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
|
||||
value=i18n("凑四句一切"),
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
gr.Markdown("gpt采样参数(无参考文本时不要太低)：")
|
||||
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
|
||||
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
|
||||
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
|
||||
inference_button = gr.Button(i18n("合成语音"), variant="primary")
|
||||
output = gr.Audio(label=i18n("输出的语音"))
|
||||
|
||||
inference_button.click(
|
||||
get_tts_wav,
|
||||
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
|
||||
[output],
|
||||
inference_button.click(
|
||||
get_tts_wav,
|
||||
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
|
||||
[output],
|
||||
)
|
||||
|
||||
gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
|
||||
with gr.Row():
|
||||
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
|
||||
button1 = gr.Button(i18n("凑四句一切"), variant="primary")
|
||||
button2 = gr.Button(i18n("凑50字一切"), variant="primary")
|
||||
button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
|
||||
button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
|
||||
button5 = gr.Button(i18n("按标点符号切"), variant="primary")
|
||||
text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
|
||||
button1.click(cut1, [text_inp], [text_opt])
|
||||
button2.click(cut2, [text_inp], [text_opt])
|
||||
button3.click(cut3, [text_inp], [text_opt])
|
||||
button4.click(cut4, [text_inp], [text_opt])
|
||||
button5.click(cut5, [text_inp], [text_opt])
|
||||
gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
|
||||
|
||||
app.queue(max_size=1022).launch(
|
||||
server_name="0.0.0.0",
|
||||
inbrowser=True,
|
||||
share=is_share,
|
||||
server_port=infer_ttswebui,
|
||||
quiet=True,
|
||||
)
|
||||
|
||||
gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
|
||||
with gr.Row():
|
||||
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
|
||||
button1 = gr.Button(i18n("凑四句一切"), variant="primary")
|
||||
button2 = gr.Button(i18n("凑50字一切"), variant="primary")
|
||||
button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
|
||||
button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
|
||||
button5 = gr.Button(i18n("按标点符号切"), variant="primary")
|
||||
text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
|
||||
button1.click(cut1, [text_inp], [text_opt])
|
||||
button2.click(cut2, [text_inp], [text_opt])
|
||||
button3.click(cut3, [text_inp], [text_opt])
|
||||
button4.click(cut4, [text_inp], [text_opt])
|
||||
button5.click(cut5, [text_inp], [text_opt])
|
||||
gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
|
||||
|
||||
app.queue(concurrency_count=511, max_size=1022).launch(
|
||||
server_name="0.0.0.0",
|
||||
inbrowser=True,
|
||||
share=is_share,
|
||||
server_port=infer_ttswebui,
|
||||
quiet=True,
|
||||
)
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user