Dev 2.3. (#242)
* Fix inputs of duration discriminator * Add LSTM * Update models.py * Update tensorboard scalar * Noise injection for minimizing modality gap * Update infer.py * support bf16 run * del unused_para flag * support bf16 config * add grad clip * fix(logger and grad):add dur grad,fix grad clip * Update webui_preprocess.py * Fix English G2P * fix(bert_gen):add pass * Pass SDP to DD * Update webui_preprocess.py * Update config.json * Update webui.py * Update chinese_bert.py * Upload webui for deploy * Update webui.py * torch.save as pt not npy * Update config.json * add freeze emo vq * Update webui_preprocess.py * Fix tone_sandhi.py * Comment up grad clip * Fix in-place addition * Add SLM discriminator * Add DDP for WD * Feat: Style text: make emotions and style similar to the style text by mixing bert (#240) (#241) * fix:(oldVersion210) Load on demand Emotion model * feat: update fastapi.py. 添加更多错误日志信息 * Switch pyopenjtalk to pyopenjtalk-prebuilt * fix: update fastapi.py. 2.2 reference适配 * Update resample.py * 修复Onnx导出的BUG (#237) * Add files via upload * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add files via upload * Add files via upload * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Delete attentions_onnx.py * Delete models_onnx.py * Add files via upload * Add files via upload * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update __init__.py * Update __init__.py * Update __init__.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- * Fix onnx * Format export * Feat: style-text and bert mixing (JA only) * Ensure the same tensor shape * Update * update gradio version * Fix * Style text for chinese and english (ver 2.2) * Style text for chinese and english (ver 2.1) * Style text in FastAPI * Translate style text desc in chinese --------- Co-authored-by: litagin02 <139731664+litagin02@users.noreply.github.com> Co-authored-by: Sora <654163754@qq.com> Co-authored-by: Sihan Wang <wangsihan1995@gmail.com> Co-authored-by: Ναρουσέ·μ·γιουμεμί·Χινακάννα <40709280+NaruseMioShirakana@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Remove CLAP * Revert "Remove CLAP" This reverts commit 62fd59bc837c580239840a2bc84b15e0663730fc. Revert * Remove CLAP * bf16 audo grad cilp * Update webui and infer utils * Update webui.py * Update webui.py * Update webui-preprocess.py * Update webui_preprocess.py --------- Co-authored-by: Sihan Wang <wangsihan1995@gmail.com> Co-authored-by: OedoSoldier <31711261+OedoSoldier@users.noreply.github.com> Co-authored-by: litagin02 <139731664+litagin02@users.noreply.github.com> Co-authored-by: Sora <654163754@qq.com> Co-authored-by: Ναρουσέ·μ·γιουμεμί·Χινακάννα <40709280+NaruseMioShirakana@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Stardust·减
556
for_deploy/webui.py
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556
for_deploy/webui.py
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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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from infer_utils import BertFeature, ClapFeature
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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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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["MKL_NUM_THREADS"] = "1"
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bert_feature_map = {
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"ZH": BertFeature(
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"./bert/chinese-roberta-wwm-ext-large",
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language="ZH",
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),
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"JP": BertFeature(
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"./bert/deberta-v2-large-japanese-char-wwm",
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language="JP",
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),
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"EN": BertFeature(
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"./bert/deberta-v3-large",
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language="EN",
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),
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}
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clap_feature = ClapFeature("./emotional/clap-htsat-fused")
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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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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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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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bert=bert_feature_map,
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clap=clap_feature,
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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 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) and skip_start
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skip_end = (idx != len(slices) - 1) and skip_end
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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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# 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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reference_audio,
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emotion,
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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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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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)
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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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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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)
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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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reference_audio,
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emotion,
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prompt_mode,
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):
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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 = []
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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 = []
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for slice in re_matching.text_matching(text):
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_speaker = slice.pop()
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temp_contant = []
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temp_lang = []
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for lang, content in slice:
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if "|" in content:
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temp = []
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temp_ = []
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for i in content.split("|"):
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if i != "":
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temp.append([i])
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temp_.append([lang])
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else:
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temp.append([])
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temp_.append([])
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temp_contant += temp
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temp_lang += temp_
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else:
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if len(temp_contant) == 0:
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temp_contant.append([])
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temp_lang.append([])
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temp_contant[-1].append(content)
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temp_lang[-1].append(lang)
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for i, j in zip(temp_lang, temp_contant):
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result.append([*zip(i, j), _speaker])
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for i, one in enumerate(result):
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skip_start = i != 0
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skip_end = i != len(result) - 1
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_speaker = one.pop()
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idx = 0
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while idx < len(one):
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text_to_generate = []
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lang_to_generate = []
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while True:
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lang, content = one[idx]
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temp_text = [content]
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if len(text_to_generate) > 0:
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text_to_generate[-1] += [temp_text.pop(0)]
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lang_to_generate[-1] += [lang]
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if len(temp_text) > 0:
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text_to_generate += [[i] for i in temp_text]
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lang_to_generate += [[lang]] * len(temp_text)
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if idx + 1 < len(one):
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idx += 1
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else:
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break
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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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print(text_to_generate, lang_to_generate)
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audio_list.extend(
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generate_audio_multilang(
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text_to_generate,
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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_to_generate,
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reference_audio,
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emotion,
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skip_start,
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skip_end,
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)
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)
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idx += 1
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elif language.lower() == "auto":
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for idx, slice in enumerate(text.split("|")):
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if slice == "":
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continue
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skip_start = idx != 0
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skip_end = idx != len(text.split("|")) - 1
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sentences_list = split_by_language(
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slice, target_languages=["zh", "ja", "en"]
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)
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idx = 0
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while idx < len(sentences_list):
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text_to_generate = []
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lang_to_generate = []
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while True:
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content, lang = sentences_list[idx]
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temp_text = [content]
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lang = lang.upper()
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if lang == "JA":
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lang = "JP"
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if len(text_to_generate) > 0:
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text_to_generate[-1] += [temp_text.pop(0)]
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lang_to_generate[-1] += [lang]
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if len(temp_text) > 0:
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text_to_generate += [[i] for i in temp_text]
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lang_to_generate += [[lang]] * len(temp_text)
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if idx + 1 < len(sentences_list):
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idx += 1
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else:
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break
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skip_start = (idx != 0) and skip_start
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skip_end = (idx != len(sentences_list) - 1) and skip_end
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print(text_to_generate, lang_to_generate)
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audio_list.extend(
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generate_audio_multilang(
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text_to_generate,
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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_to_generate,
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reference_audio,
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emotion,
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skip_start,
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skip_end,
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)
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)
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idx += 1
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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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)
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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 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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|
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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():
|
||||
with gr.Column():
|
||||
text = gr.TextArea(
|
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label="输入文本内容",
|
||||
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？
|
||||
[说话人3]<zh>谢谢。<jp>どういたしまして。
|
||||
...
|
||||
另外，所有的语言选项都可以用'|'分割长段实现分句生成。
|
||||
""",
|
||||
)
|
||||
trans = gr.Button("中翻日", variant="primary")
|
||||
slicer = gr.Button("快速切分", variant="primary")
|
||||
speaker = gr.Dropdown(
|
||||
choices=speakers, value=speakers[0], label="Speaker"
|
||||
)
|
||||
_ = gr.Markdown(
|
||||
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"
|
||||
)
|
||||
prompt_mode = gr.Radio(
|
||||
["Text prompt", "Audio prompt"],
|
||||
label="Prompt Mode",
|
||||
value="Text prompt",
|
||||
)
|
||||
text_prompt = gr.Textbox(
|
||||
label="Text prompt",
|
||||
placeholder="用文字描述生成风格。如：Happy",
|
||||
value="Happy",
|
||||
visible=True,
|
||||
)
|
||||
audio_prompt = gr.Audio(
|
||||
label="Audio prompt", type="filepath", visible=False
|
||||
)
|
||||
sdp_ratio = gr.Slider(
|
||||
minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
|
||||
)
|
||||
noise_scale = gr.Slider(
|
||||
minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise"
|
||||
)
|
||||
noise_scale_w = gr.Slider(
|
||||
minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise_W"
|
||||
)
|
||||
length_scale = gr.Slider(
|
||||
minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
|
||||
)
|
||||
language = gr.Dropdown(
|
||||
choices=languages, value=languages[0], label="Language"
|
||||
)
|
||||
btn = gr.Button("生成音频！", variant="primary")
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
interval_between_sent = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=5,
|
||||
value=0.2,
|
||||
step=0.1,
|
||||
label="句间停顿(秒)，勾选按句切分才生效",
|
||||
)
|
||||
interval_between_para = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=10,
|
||||
value=1,
|
||||
step=0.1,
|
||||
label="段间停顿(秒)，需要大于句间停顿才有效",
|
||||
)
|
||||
opt_cut_by_sent = gr.Checkbox(
|
||||
label="按句切分 在按段落切分的基础上再按句子切分文本"
|
||||
)
|
||||
slicer = gr.Button("切分生成", variant="primary")
|
||||
text_output = gr.Textbox(label="状态信息")
|
||||
audio_output = gr.Audio(label="输出音频")
|
||||
# explain_image = gr.Image(
|
||||
# label="参数解释信息",
|
||||
# show_label=True,
|
||||
# show_share_button=False,
|
||||
# show_download_button=False,
|
||||
# value=os.path.abspath("./img/参数说明.png"),
|
||||
# )
|
||||
btn.click(
|
||||
tts_fn,
|
||||
inputs=[
|
||||
text,
|
||||
speaker,
|
||||
sdp_ratio,
|
||||
noise_scale,
|
||||
noise_scale_w,
|
||||
length_scale,
|
||||
language,
|
||||
audio_prompt,
|
||||
text_prompt,
|
||||
prompt_mode,
|
||||
],
|
||||
outputs=[text_output, audio_output],
|
||||
)
|
||||
|
||||
trans.click(
|
||||
translate,
|
||||
inputs=[text],
|
||||
outputs=[text],
|
||||
)
|
||||
slicer.click(
|
||||
tts_split,
|
||||
inputs=[
|
||||
text,
|
||||
speaker,
|
||||
sdp_ratio,
|
||||
noise_scale,
|
||||
noise_scale_w,
|
||||
length_scale,
|
||||
language,
|
||||
opt_cut_by_sent,
|
||||
interval_between_para,
|
||||
interval_between_sent,
|
||||
audio_prompt,
|
||||
text_prompt,
|
||||
],
|
||||
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],
|
||||
)
|
||||
|
||||
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)
|
||||
Reference in New Issue
Block a user