558 lines
18 KiB
Python
558 lines
18 KiB
Python
"""
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Original `webui.py` for Bert-VITS2, not working with Style-Bert-VITS2 yet.
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"""
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# flake8: noqa: E402
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import os
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import logging
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import re_matching
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from tools.sentence import split_by_language
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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 utils
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from infer import infer, latest_version, get_net_g, infer_multilang
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import gradio as gr
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import webbrowser
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import numpy as np
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from config import config
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from tools.translate import translate
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import librosa
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net_g = None
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device = config.webui_config.device
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if device == "mps":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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def generate_audio(
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slices,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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speaker,
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language,
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reference_audio,
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emotion,
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style_text,
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style_weight,
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skip_start=False,
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skip_end=False,
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):
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audio_list = []
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# silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
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with torch.no_grad():
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for idx, piece in enumerate(slices):
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skip_start = idx != 0
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skip_end = idx != len(slices) - 1
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audio = infer(
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piece,
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reference_audio=reference_audio,
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emotion=emotion,
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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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hps=hps,
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net_g=net_g,
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device=device,
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skip_start=skip_start,
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skip_end=skip_end,
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assist_text=style_text,
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assist_text_weight=style_weight,
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)
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audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
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audio_list.append(audio16bit)
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return audio_list
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def generate_audio_multilang(
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slices,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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speaker,
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language,
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reference_audio,
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emotion,
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skip_start=False,
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skip_end=False,
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):
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audio_list = []
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# silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
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with torch.no_grad():
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for idx, piece in enumerate(slices):
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skip_start = idx != 0
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skip_end = idx != len(slices) - 1
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audio = infer_multilang(
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piece,
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reference_audio=reference_audio,
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emotion=emotion,
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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[idx],
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hps=hps,
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net_g=net_g,
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device=device,
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skip_start=skip_start,
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skip_end=skip_end,
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)
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audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
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audio_list.append(audio16bit)
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return audio_list
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def tts_split(
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text: str,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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language,
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cut_by_sent,
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interval_between_para,
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interval_between_sent,
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reference_audio,
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emotion,
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style_text,
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style_weight,
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):
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while text.find("\n\n") != -1:
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text = text.replace("\n\n", "\n")
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text = text.replace("|", "")
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para_list = re_matching.cut_para(text)
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para_list = [p for p in para_list if p != ""]
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audio_list = []
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for p in para_list:
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if not cut_by_sent:
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audio_list += process_text(
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p,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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language,
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reference_audio,
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emotion,
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style_text,
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style_weight,
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)
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silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
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audio_list.append(silence)
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else:
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audio_list_sent = []
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sent_list = re_matching.cut_sent(p)
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sent_list = [s for s in sent_list if s != ""]
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for s in sent_list:
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audio_list_sent += process_text(
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s,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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language,
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reference_audio,
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emotion,
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style_text,
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style_weight,
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)
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silence = np.zeros((int)(44100 * interval_between_sent))
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audio_list_sent.append(silence)
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if (interval_between_para - interval_between_sent) > 0:
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silence = np.zeros(
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(int)(44100 * (interval_between_para - interval_between_sent))
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)
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audio_list_sent.append(silence)
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audio16bit = gr.processing_utils.convert_to_16_bit_wav(
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np.concatenate(audio_list_sent)
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) # 对完整句子做音量归一
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audio_list.append(audio16bit)
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audio_concat = np.concatenate(audio_list)
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return ("Success", (hps.data.sampling_rate, audio_concat))
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def process_mix(slice):
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_speaker = slice.pop()
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_text, _lang = [], []
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for lang, content in slice:
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content = content.split("|")
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content = [part for part in content if part != ""]
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if len(content) == 0:
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continue
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if len(_text) == 0:
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_text = [[part] for part in content]
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_lang = [[lang] for part in content]
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else:
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_text[-1].append(content[0])
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_lang[-1].append(lang)
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if len(content) > 1:
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_text += [[part] for part in content[1:]]
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_lang += [[lang] for part in content[1:]]
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return _text, _lang, _speaker
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def process_auto(text):
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_text, _lang = [], []
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for slice in text.split("|"):
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if slice == "":
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continue
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temp_text, temp_lang = [], []
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sentences_list = split_by_language(slice, target_languages=["zh", "ja", "en"])
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for sentence, lang in sentences_list:
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if sentence == "":
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continue
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temp_text.append(sentence)
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if lang == "ja":
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lang = "jp"
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temp_lang.append(lang.upper())
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_text.append(temp_text)
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_lang.append(temp_lang)
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return _text, _lang
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def process_text(
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text: str,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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language,
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reference_audio,
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emotion,
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style_text=None,
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style_weight=0,
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):
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audio_list = []
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if language == "mix":
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bool_valid, str_valid = re_matching.validate_text(text)
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if not bool_valid:
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return str_valid, (
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hps.data.sampling_rate,
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np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
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)
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for slice in re_matching.text_matching(text):
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_text, _lang, _speaker = process_mix(slice)
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if _speaker is None:
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continue
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print(f"Text: {_text}\nLang: {_lang}")
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audio_list.extend(
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generate_audio_multilang(
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_text,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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_speaker,
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_lang,
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reference_audio,
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emotion,
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)
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)
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elif language.lower() == "auto":
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_text, _lang = process_auto(text)
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print(f"Text: {_text}\nLang: {_lang}")
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audio_list.extend(
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generate_audio_multilang(
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_text,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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speaker,
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_lang,
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reference_audio,
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emotion,
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)
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)
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else:
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audio_list.extend(
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generate_audio(
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text.split("|"),
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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speaker,
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language,
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reference_audio,
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emotion,
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style_text,
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style_weight,
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)
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)
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return audio_list
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def tts_fn(
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text: str,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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language,
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reference_audio,
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emotion,
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prompt_mode,
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style_text=None,
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style_weight=0,
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):
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if style_text == "":
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style_text = None
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if prompt_mode == "Audio prompt":
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if reference_audio == None:
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return ("Invalid audio prompt", None)
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else:
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reference_audio = load_audio(reference_audio)[1]
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else:
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reference_audio = None
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audio_list = process_text(
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text,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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language,
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reference_audio,
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emotion,
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style_text,
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style_weight,
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)
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audio_concat = np.concatenate(audio_list)
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return "Success", (hps.data.sampling_rate, audio_concat)
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def format_utils(text, speaker):
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_text, _lang = process_auto(text)
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res = f"[{speaker}]"
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for lang_s, content_s in zip(_lang, _text):
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for lang, content in zip(lang_s, content_s):
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res += f"<{lang.lower()}>{content}"
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res += "|"
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return "mix", res[:-1]
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def load_audio(path):
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audio, sr = librosa.load(path, 48000)
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# audio = librosa.resample(audio, 44100, 48000)
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return sr, audio
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def gr_util(item):
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if item == "Text prompt":
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return {"visible": True, "__type__": "update"}, {
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"visible": False,
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"__type__": "update",
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}
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else:
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return {"visible": False, "__type__": "update"}, {
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"visible": True,
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"__type__": "update",
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}
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if __name__ == "__main__":
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if config.webui_config.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(config.webui_config.config_path)
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# 若config.json中未指定版本则默认为最新版本
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version = hps.version if hasattr(hps, "version") else latest_version
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net_g = get_net_g(
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model_path=config.webui_config.model, version=version, device=device, hps=hps
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)
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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", "EN", "mix", "auto"]
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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="输入文本内容",
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placeholder="""
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如果你选择语言为\'mix\'，必须按照格式输入，否则报错:
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格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi):
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[说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。
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[说话人2]<zh>你好吗？<jp>元気ですか？
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[说话人3]<zh>谢谢。<jp>どういたしまして。
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...
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另外，所有的语言选项都可以用'|'分割长段实现分句生成。
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""",
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)
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trans = gr.Button("中翻日", variant="primary")
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slicer = gr.Button("快速切分", variant="primary")
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formatter = gr.Button("检测语言，并整理为 MIX 格式", variant="primary")
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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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_ = gr.Markdown(
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value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n",
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visible=False,
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)
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prompt_mode = gr.Radio(
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["Text prompt", "Audio prompt"],
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label="Prompt Mode",
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value="Text prompt",
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visible=False,
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)
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text_prompt = gr.Textbox(
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label="Text prompt",
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placeholder="用文字描述生成风格。如：Happy",
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value="Happy",
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visible=False,
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)
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audio_prompt = gr.Audio(
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label="Audio prompt", type="filepath", visible=False
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)
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sdp_ratio = gr.Slider(
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minimum=0, maximum=1, value=0.5, 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"
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)
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noise_scale_w = gr.Slider(
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minimum=0.1, maximum=2, value=0.9, step=0.1, label="Noise_W"
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)
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length_scale = gr.Slider(
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minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
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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("生成音频！", variant="primary")
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with gr.Column():
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with gr.Accordion("融合文本语义", open=False):
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gr.Markdown(
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value="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
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"**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
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"效果较不明确，留空即为不使用该功能"
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)
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style_text = gr.Textbox(label="辅助文本")
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style_weight = gr.Slider(
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minimum=0,
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maximum=1,
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value=0.7,
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step=0.1,
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label="Weight",
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info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
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)
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with gr.Row():
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with gr.Column():
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interval_between_sent = gr.Slider(
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minimum=0,
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maximum=5,
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value=0.2,
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step=0.1,
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label="句间停顿(秒)，勾选按句切分才生效",
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)
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interval_between_para = gr.Slider(
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minimum=0,
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maximum=10,
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value=1,
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step=0.1,
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label="段间停顿(秒)，需要大于句间停顿才有效",
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)
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opt_cut_by_sent = gr.Checkbox(
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label="按句切分 在按段落切分的基础上再按句子切分文本"
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)
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slicer = gr.Button("切分生成", variant="primary")
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text_output = gr.Textbox(label="状态信息")
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audio_output = gr.Audio(label="输出音频")
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# explain_image = gr.Image(
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# label="参数解释信息",
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# show_label=True,
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# show_share_button=False,
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# show_download_button=False,
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# value=os.path.abspath("./img/参数说明.png"),
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# )
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btn.click(
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tts_fn,
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inputs=[
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text,
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speaker,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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||
length_scale,
|
||
language,
|
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audio_prompt,
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text_prompt,
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prompt_mode,
|
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style_text,
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style_weight,
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],
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outputs=[text_output, audio_output],
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)
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trans.click(
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translate,
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inputs=[text],
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outputs=[text],
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)
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slicer.click(
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tts_split,
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inputs=[
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text,
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speaker,
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sdp_ratio,
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noise_scale,
|
||
noise_scale_w,
|
||
length_scale,
|
||
language,
|
||
opt_cut_by_sent,
|
||
interval_between_para,
|
||
interval_between_sent,
|
||
audio_prompt,
|
||
text_prompt,
|
||
style_text,
|
||
style_weight,
|
||
],
|
||
outputs=[text_output, audio_output],
|
||
)
|
||
|
||
prompt_mode.change(
|
||
lambda x: gr_util(x),
|
||
inputs=[prompt_mode],
|
||
outputs=[text_prompt, audio_prompt],
|
||
)
|
||
|
||
audio_prompt.upload(
|
||
lambda x: load_audio(x),
|
||
inputs=[audio_prompt],
|
||
outputs=[audio_prompt],
|
||
)
|
||
|
||
formatter.click(
|
||
format_utils,
|
||
inputs=[text, speaker],
|
||
outputs=[language, text],
|
||
)
|
||
|
||
print("推理页面已开启!")
|
||
webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
|
||
app.launch(share=config.webui_config.share, server_port=config.webui_config.port)
|