349 lines
12 KiB
Python
349 lines
12 KiB
Python
# 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, sentence_split
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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
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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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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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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) and skip_start
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skip_end = (idx != len(slices) - 1) and skip_end
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audio = infer(
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piece,
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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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)
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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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# audio_list.append(silence) # 将静音添加到列表中
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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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):
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if language == "mix":
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return ("invalid", None)
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while text.find("\n\n") != -1:
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text = text.replace("\n\n", "\n")
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para_list = re_matching.cut_para(text)
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audio_list = []
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if not cut_by_sent:
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for idx, p in enumerate(para_list):
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skip_start = idx != 0
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skip_end = idx != len(para_list) - 1
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audio = infer(
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p,
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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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)
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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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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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for idx, p in enumerate(para_list):
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skip_start = idx != 0
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skip_end = idx != len(para_list) - 1
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audio_list_sent = []
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sent_list = re_matching.cut_sent(p)
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for idx, s in enumerate(sent_list):
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skip_start = (idx != 0) and skip_start
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skip_end = (idx != len(sent_list) - 1) and skip_end
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audio = infer(
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s,
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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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)
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audio_list_sent.append(audio)
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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", (44100, audio_concat))
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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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):
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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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result = re_matching.text_matching(text)
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for idx, one in enumerate(result):
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skip_start = idx != 0
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skip_end = idx != len(result) - 1
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_speaker = one.pop()
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for idx, (lang, content) in enumerate(one):
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skip_start = (idx != 0) and skip_start
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skip_end = (idx != len(one) - 1) and skip_end
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audio_list.extend(
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generate_audio(
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content.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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lang,
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skip_start,
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skip_end,
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)
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)
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elif language.lower() == "auto":
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sentences_list = split_by_language(text, target_languages=["zh", "ja", "en"])
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for idx, (sentences, lang) in enumerate(sentences_list):
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skip_start = idx != 0
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skip_end = idx != len(sentences_list) - 1
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lang = lang.upper()
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if lang == "JA":
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lang = "JP"
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sentences = sentence_split(sentences, max=250)
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for idx, content in enumerate(sentences):
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skip_start = (idx != 0) and skip_start
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skip_end = (idx != len(sentences) - 1) and skip_end
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audio_list.extend(
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generate_audio(
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content.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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lang,
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skip_start,
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skip_end,
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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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)
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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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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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speaker = gr.Dropdown(
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choices=speakers, value=speakers[0], label="选择说话人"
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)
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sdp_ratio = gr.Slider(
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minimum=0, maximum=1, value=0.2, step=0.1, label="SDP/DP混合比"
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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="感情"
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)
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noise_scale_w = gr.Slider(
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minimum=0.1, maximum=2, value=0.8, step=0.1, label="音素长度"
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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="语速"
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)
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language = gr.Dropdown(
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choices=languages, value=languages[0], label="选择语言(新增mix混合选项)"
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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.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,
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language,
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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,
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noise_scale_w,
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length_scale,
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language,
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opt_cut_by_sent,
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interval_between_para,
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interval_between_sent,
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],
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outputs=[text_output, audio_output],
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)
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print("推理页面已开启!")
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webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
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app.launch(share=config.webui_config.share, server_port=config.webui_config.port)
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