mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-01-31 10:42:52 +08:00
**Subject:** *Add loss graph functionality for Gradio interface*
**Body:** * Implements helper functions to retrieve and display loss graphs for different projects. * Integrates with Gradio to provide interactive tabs with loss graph images. * Handles project selection and updates loss graphs accordingly. * Includes helper functions for generating and saving loss graphs from TensorBoard logs
This commit is contained in:
Wernervanrun
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1b7add90ea
@ -133,5 +133,10 @@
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"音调设置": "Pitch settings",
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"音频设备": "Audio device",
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"音高算法": "pitch detection algorithm",
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"额外推理时长": "Extra inference time"
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"额外推理时长": "Extra inference time",
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"损失图": "Loss Graph",
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"选择语音": "Select voice",
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"更新损失图": "Update Loss Graph",
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"更新语音列表": "Update Voice List",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Training Progress Overview: Lower values indicate better model performance. For detailed insights, explore TensorBoard."
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}
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@ -133,5 +133,10 @@
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"音调设置": "Ajuste de tono",
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"音频设备": "Dispositivo de audio",
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"音高算法": "Algoritmo de tono",
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"额外推理时长": "Tiempo de inferencia adicional"
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"额外推理时长": "Tiempo de inferencia adicional",
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"损失图": "Gráfico de pérdida",
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"选择语音": "Seleccione el audio",
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"更新损失图": "Actualizar gráfico de pérdida",
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"更新语音列表": "Actualizar lista de audio",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Resumen del progreso de entrenamiento: cuanto menor sea el valor, mejor será el rendimiento del modelo. Para obtener una visión detallada, explore TensorBoard."
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}
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@ -133,5 +133,10 @@
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"音调设置": "Réglages de la hauteur",
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"音频设备": "Périphérique audio",
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"音高算法": "algorithme de détection de la hauteur",
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"额外推理时长": "Temps d'inférence supplémentaire"
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"额外推理时长": "Temps d'inférence supplémentaire",
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"损失图": "Graphique de perte",
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"选择语音": "Sélectionner la voix",
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"更新损失图": "Actualiser le graphique de perte",
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"更新语音列表": "Actualiser la liste des voix",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Aperçu de la progression de l'entraînement : les valeurs inférieures indiquent des performances de modèle plus élevées. Pour une compréhension détaillée, explorez TensorBoard."
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}
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@ -133,5 +133,10 @@
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"音调设置": "Impostazioni del tono",
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"音频设备": "Dispositivo audio",
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"音高算法": "音高算法",
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"额外推理时长": "Tempo di inferenza extra"
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"额外推理时长": "Tempo di inferenza extra",
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"损失图": "Grafico delle perdite",
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"选择语音": "Seleziona la voce",
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"更新损失图": "Aggiornare il grafico delle perdite",
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"更新语音列表": "Aggiornare l'elenco delle voci",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Riepilogo del progresso dell'addestramento: i valori più bassi indicano prestazioni di modello migliori. Per una comprensione più approfondita, esplora TensorBoard."
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}
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@ -133,5 +133,10 @@
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"音调设置": "音程設定",
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"音频设备": "オーディオデバイス",
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"音高算法": "ピッチアルゴリズム",
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"额外推理时长": "追加推論時間"
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"额外推理时长": "追加推論時間",
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"损失图": "損失グラフ",
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"选择语音": "音声を選択",
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"更新损失图": "損失グラフを更新する",
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"更新语音列表": "音声リストを更新する",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "トレーニング進行状況の概要：値が低いほど、モデルの性能が良い。詳細な見解を得るには、TensorBoardを探索してください。"
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}
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@ -133,5 +133,10 @@
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"音调设置": "음조 설정",
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"音频设备": "音频设备",
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"音高算法": "음높이 알고리즘",
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"额外推理时长": "추가 추론 시간"
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"额外推理时长": "추가 추론 시간",
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"损失图": "손실 그래프",
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"选择语音": "음성 선택",
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"更新损失图": "손실 그래프 업데이트",
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"更新语音列表": "음성 목록 업데이트",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "훈련 진행 개요: 값이 낮을수록 모델 성능이 좋습니다. 자세한 내용은 TensorBoard를 탐색하세요."
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}
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@ -133,5 +133,10 @@
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"音调设置": "Configurações de tom",
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"音频设备": "音频设备",
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"音高算法": "Algoritmo de detecção de pitch",
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"额外推理时长": "Tempo extra de inferência"
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"额外推理时长": "Tempo extra de inferência",
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"损失图": "Gráfico de perda",
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"选择语音": "Selecione o áudio",
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"更新损失图": "Atualizar gráfico de perda",
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"更新语音列表": "Atualizar lista de áudio",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Visão geral do progresso do treinamento: quanto menor o valor, melhor o desempenho do modelo. Para obter insights detalhados, explore o TensorBoard."
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}
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@ -133,5 +133,10 @@
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"音调设置": "Настройка высоты звука",
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"音频设备": "Аудиоустройство",
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"音高算法": "Алгоритм оценки высоты звука",
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"额外推理时长": "Доп. время переработки"
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"额外推理时长": "Доп. время переработки",
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"损失图": "График потерь",
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"选择语音": "Выберите голос",
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"更新损失图": "Обновить график потерь",
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"更新语音列表": "Обновить список голосов",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Обзор обучения: чем ниже значение, тем лучше качество модели. Подробнее см. TensorBoard."
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}
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@ -133,5 +133,10 @@
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"音调设置": "Pitch ayarları",
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"音频设备": "Ses cihazı",
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"音高算法": "音高算法",
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"额外推理时长": "Ekstra çıkartma süresi"
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"额外推理时长": "Ekstra çıkartma süresi",
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"损失图": "Kayıp grafiği",
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"选择语音": "Konuşma seç",
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"更新损失图": "Kayıp grafiğini güncelle",
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"更新语音列表": "Konuşma listesini güncelle",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "Eğitim ilerlemesi özeti: Düşük değerler, model performansının daha iyi olduğunu gösterir. Daha fazla ayrıntı için TensorBoard'u keşfedin."
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}
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@ -133,5 +133,10 @@
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"音调设置": "音调设置",
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"音频设备": "音频设备",
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"音高算法": "音高算法",
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"额外推理时长": "额外推理时长"
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"额外推理时长": "额外推理时长",
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"损失图": "损失图",
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"选择语音": "选择语音",
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"更新损失图": "更新损失图",
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"更新语音列表": "更新语音列表",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。"
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}
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@ -133,5 +133,10 @@
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"音调设置": "音調設定",
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"音频设备": "音訊設備",
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"音高算法": "音高演算法",
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"额外推理时长": "額外推理時長"
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"额外推理时长": "額外推理時長",
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"损失图": "損失圖",
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"选择语音": "選擇語音",
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"更新损失图": "更新損失圖",
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"更新语音列表": "更新語音列表",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "訓練進度概覽：值越低，模型性能越好。如需詳細見解，請探索 TensorBoard。"
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}
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@ -133,5 +133,10 @@
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"音调设置": "音調設定",
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"音频设备": "音訊設備",
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"音高算法": "音高演算法",
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"额外推理时长": "額外推理時長"
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"额外推理时长": "額外推理時長",
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"损失图": "損失圖",
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"选择语音": "選擇語音",
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"更新损失图": "更新損失圖",
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"更新语音列表": "更新語音列表",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "訓練進度概覽：值越低，模型性能越好。如需詳細見解，請探索 TensorBoard。"
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}
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"音调设置": "音調設定",
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"音频设备": "音訊設備",
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"音高算法": "音高演算法",
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"额外推理时长": "額外推理時長"
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"额外推理时长": "額外推理時長",
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"损失图": "損失圖",
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"选择语音": "选择语音",
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"更新损失图": "更新損失圖",
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"更新语音列表": "更新语音列表",
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。": "訓練進度概覽：值越低，模型性能越好。如需詳細見解，請探索 TensorBoard。"
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}
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136
infer-web.py
136
infer-web.py
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# -*- coding: utf-8 -*-
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import os
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import sys
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from dotenv import load_dotenv
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@ -806,6 +807,100 @@ def change_f0_method(f0method8):
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return {"visible": visible, "__type__": "update"}
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# start tab loss graph helper functions
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desired_tags = ["loss_d_total", "loss_g_total", "loss_g_fm", "loss_g_mel", "loss_g_kl"]
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def get_projects():
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"""
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Get the list of projects.
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"""
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return [name for name in os.listdir(index_root) if os.path.isdir(os.path.join(index_root, name)) and name != 'mute' and os.path.isdir(os.path.join(index_root, name, 'loss_graphs'))]
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def get_loss_graph_images(selection):
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"""
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Gets loss graph images for a given project, assuming filenames match desired order.
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Args:
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selection (str): Project name.
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Returns:
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dict: A dictionary of image paths keyed by desired_tags.
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"""
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loss_graphs_path = os.path.join(index_root, selection, 'loss_graphs')
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if not os.path.exists(loss_graphs_path):
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print(f"Directory not found: {loss_graphs_path}")
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return {}
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graphs = {}
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for tag in desired_tags:
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image_path = os.path.join(loss_graphs_path, f"{tag}.jpeg")
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if os.path.exists(image_path):
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graphs[tag] = image_path
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return graphs
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def get_loss_graph_tabs(project):
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"""
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Create Gradio Tabs and Image fields for the loss graphs.
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Args:
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project (str): Project name.
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Returns:
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gr.Tabs, list: A tuple containing the Gradio Tabs component and a list of image fields.
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"""
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loss_graph_tabs = gr.Tabs()
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loss_graph_images = get_loss_graph_images(project)
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loss_graph_image_fields = {}
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with loss_graph_tabs:
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for tag, image_path in loss_graph_images.items():
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with gr.TabItem(tag):
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image_field = gr.Image(value=image_path, width="100%")
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loss_graph_image_fields[tag] = image_field
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return loss_graph_tabs, list(loss_graph_image_fields.values())
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def update_loss_graph_images(selection):
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"""
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Update the loss graph images for a given project.
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"""
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loss_graph_images = get_loss_graph_images(selection)
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updated_values = []
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for i, tag in enumerate(desired_tags):
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if i < len(image_fields):
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if tag in loss_graph_images:
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image_path = loss_graph_images[tag]
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if os.path.exists(image_path) and os.path.isfile(image_path):
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updated_values.append(image_path)
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else:
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print(f"Warning: Image file does not exist or is not a file: {image_path}")
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updated_values.append(None)
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else:
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print(f"Warning: No image found for tag: {tag}")
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updated_values.append(None)
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return updated_values
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def update_projects():
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"""
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Update the list of projects.
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"""
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projects = get_projects()
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return {"choices": sorted(projects), "__type__": "update"}
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projects = get_projects()
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# Check if there are any projects before accessing
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if projects:
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default_project = projects[0]
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default_loss_graph_images = get_loss_graph_images(projects[0])
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else:
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print("No projects found.")
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default_project = None
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default_loss_graph_images = []
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# gradio app
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with gr.Blocks(title="RVC WebUI") as app:
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gr.Markdown("## RVC WebUI")
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gr.Markdown(
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@ -1420,7 +1515,48 @@ with gr.Blocks(title="RVC WebUI") as app:
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info3,
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api_name="train_start_all",
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)
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with gr.TabItem(i18n("损失图")):
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gr.Markdown(
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value=i18n(
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"训练进度概览：值越低，模型性能越好。如需详细见解，请探索 TensorBoard。"
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)
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)
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with gr.Row():
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voice_list_dropdown = gr.Dropdown(
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label=i18n("选择语音"),
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choices=sorted(projects),
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interactive=True,
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value=default_project
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)
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with gr.Column():
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update_voice_list_button = gr.Button(
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i18n("更新语音列表"),
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variant="primary"
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)
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update_loss_graph_button = gr.Button(
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i18n("更新损失图"),
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variant="primary"
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)
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update_voice_list_button.click(
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fn=update_projects,
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inputs=[],
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outputs=[voice_list_dropdown],
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api_name="infer_refresh"
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)
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with gr.Row():
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tabs, image_fields = get_loss_graph_tabs(default_project)
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voice_list_dropdown.change(
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fn=update_loss_graph_images,
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inputs=voice_list_dropdown,
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outputs=image_fields
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)
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update_loss_graph_button.click(
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fn=update_loss_graph_images,
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inputs=voice_list_dropdown,
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outputs=image_fields
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)
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with gr.TabItem(i18n("ckpt处理")):
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with gr.Group():
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gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
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128
infer/lib/train/graph_generation.py
Normal file
128
infer/lib/train/graph_generation.py
Normal file
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import os
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from typing import List
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import matplotlib.pyplot as plt
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import logging
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# Suppress TensorBoard event processing logs
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logging.getLogger('tensorboard').setLevel(logging.ERROR)
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# Import TensorBoard modules after setting logging level
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from tensorboard.backend.event_processing import event_accumulator
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def generate_loss_graphs(log_dir):
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"""
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Generates and saves plots for the given TensorBoard logs.
|
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Args:
|
||||
log_dir (str): Directory with TensorBoard logs.
|
||||
"""
|
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scalar_data = extract_scalar_data(log_dir)
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plot_scalar_data(scalar_data, log_dir)
|
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|
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def smooth(scalars: List[float], weight: float) -> List[float]:
|
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"""
|
||||
Smooths the given list of scalars using exponential smoothing.
|
||||
|
||||
Args:
|
||||
scalars (List[float]): List of scalar values to be smoothed.
|
||||
weight (float): Weight for smoothing, between 0 and 1.
|
||||
|
||||
Returns:
|
||||
List[float]: Smoothed scalar values.
|
||||
"""
|
||||
last = scalars[0]
|
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smoothed = []
|
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for point in scalars:
|
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smoothed_val = last * weight + (1 - weight) * point
|
||||
smoothed.append(smoothed_val)
|
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last = smoothed_val
|
||||
return smoothed
|
||||
|
||||
def extract_scalar_data(log_dir):
|
||||
"""
|
||||
Extracts specific scalar data from TensorBoard logs.
|
||||
|
||||
Args:
|
||||
log_dir (str): Directory with TensorBoard logs.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary where keys are scalar names and values are lists of scalar events.
|
||||
"""
|
||||
|
||||
ea = event_accumulator.EventAccumulator(log_dir)
|
||||
ea.Reload()
|
||||
|
||||
scalar_data = {}
|
||||
desired_tags = ["loss/d/total", "loss/g/total", "loss/g/fm", "loss/g/mel", "loss/g/kl"]
|
||||
|
||||
for tag in desired_tags:
|
||||
if tag in ea.Tags()['scalars']:
|
||||
scalar_events = ea.Scalars(tag)
|
||||
scalar_data[tag] = {}
|
||||
for event in scalar_events:
|
||||
if event.step not in scalar_data[tag]:
|
||||
scalar_data[tag][event.step] = [event.value]
|
||||
else:
|
||||
scalar_data[tag][event.step].append(event.value)
|
||||
# Calculate the average for each step. Restarting training can cause multiple events for the same step.
|
||||
scalar_data[tag] = {step: sum(values) / len(values) for step, values in scalar_data[tag].items()}
|
||||
else:
|
||||
print(f"Tag: {tag} not found in the TensorBoard logs.")
|
||||
|
||||
return scalar_data
|
||||
|
||||
def sanitize_filename(filename):
|
||||
"""
|
||||
Sanitize the filename by replacing invalid characters with underscores.
|
||||
|
||||
Args:
|
||||
filename (str): The original filename or tag to sanitize.
|
||||
|
||||
Returns:
|
||||
str: Sanitized filename.
|
||||
"""
|
||||
# Replace slashes and other invalid characters with underscores
|
||||
return filename.replace('/', '_').replace('\\', '_')
|
||||
|
||||
def plot_scalar_data(scalar_data, log_dir, output_dir="loss_graphs", smooth_weight=0.75):
|
||||
"""
|
||||
Generates and saves plots for the given scalar data, with optional smoothing.
|
||||
|
||||
Args:
|
||||
scalar_data (dict): A dictionary where keys are scalar names and values are lists of scalar events.
|
||||
log_dir (str): Base directory where the generated JPEG files will be saved.
|
||||
output_dir (str): Subdirectory under `log_dir` where the JPEG files will be saved.
|
||||
smooth_weight (float): Weight for smoothing, between 0 and 1.
|
||||
"""
|
||||
loss_graph_dir = os.path.join(log_dir, output_dir)
|
||||
if not os.path.exists(loss_graph_dir):
|
||||
os.makedirs(loss_graph_dir)
|
||||
|
||||
for tag, events in scalar_data.items():
|
||||
# Sanitize the tag for use in the filename
|
||||
sanitized_tag = sanitize_filename(tag)
|
||||
file_path = os.path.join(loss_graph_dir, f'{sanitized_tag}.jpeg')
|
||||
|
||||
# Extract steps and values from the scalar events
|
||||
steps = list(events.keys())
|
||||
values = list(events.values())
|
||||
|
||||
# Print the last tag, step, and value
|
||||
if steps and values: # Ensure that the list is not empty
|
||||
last_step = steps[-1]
|
||||
last_value = values[-1]
|
||||
print(f'Last entry - Tag: {tag}, Step: {last_step}, Value: {last_value}')
|
||||
|
||||
# Apply smoothing
|
||||
smoothed_values = smooth(values, smooth_weight)
|
||||
|
||||
plt.figure(figsize=(20, 12))
|
||||
plt.plot(steps, values, label=f'{tag} (original)')
|
||||
plt.plot(steps, smoothed_values, label=f'{tag} (smoothed)', linestyle='--')
|
||||
plt.xlabel('Steps')
|
||||
plt.ylabel(tag)
|
||||
plt.title(f'{tag} over time')
|
||||
plt.yscale('log') # Set y-axis to logarithmic scale for better visualization, reminder another approach could be identify a cutoff point and disregard a few datapoints at the beginning of the training
|
||||
plt.grid(True)
|
||||
plt.legend()
|
||||
plt.savefig(file_path)
|
||||
plt.close()
|
||||
@ -9,7 +9,7 @@ sys.path.append(os.path.join(now_dir))
|
||||
|
||||
import datetime
|
||||
|
||||
from infer.lib.train import utils
|
||||
from infer.lib.train import utils, graph_generation
|
||||
|
||||
hps = utils.get_hparams()
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
|
||||
@ -558,6 +558,7 @@ def train_and_evaluate(
|
||||
images=image_dict,
|
||||
scalars=scalar_dict,
|
||||
)
|
||||
graph_generation.generate_loss_graphs(hps.model_dir)
|
||||
global_step += 1
|
||||
# /Run steps
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user