**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

i18n/locale/en_US.json

@@ -133,5 +133,10 @@
     "音调设置": "Pitch settings",
     "音频设备": "Audio device",
     "音高算法": "pitch detection algorithm",
-    "额外推理时长": "Extra inference time"
+    "额外推理时长": "Extra inference time",
+    "损失图": "Loss Graph",
+    "选择语音": "Select voice",
+    "更新损失图": "Update Loss Graph",
+    "更新语音列表": "Update Voice List",
+    "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."
 }

i18n/locale/es_ES.json

@@ -133,5 +133,10 @@
     "音调设置": "Ajuste de tono",
     "音频设备": "Dispositivo de audio",
     "音高算法": "Algoritmo de tono",
-    "额外推理时长": "Tiempo de inferencia adicional"
+    "额外推理时长": "Tiempo de inferencia adicional",
+    "损失图": "Gráfico de pérdida",
+    "选择语音": "Seleccione el audio",
+    "更新损失图": "Actualizar gráfico de pérdida",
+    "更新语音列表": "Actualizar lista de audio",
+    "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."
 }

i18n/locale/fr_FR.json

@@ -133,5 +133,10 @@
     "音调设置": "Réglages de la hauteur",
     "音频设备": "Périphérique audio",
     "音高算法": "algorithme de détection de la hauteur",
-    "额外推理时长": "Temps d'inférence supplémentaire"
+    "额外推理时长": "Temps d'inférence supplémentaire",
+    "损失图": "Graphique de perte",
+    "选择语音": "Sélectionner la voix",
+    "更新损失图": "Actualiser le graphique de perte",
+    "更新语音列表": "Actualiser la liste des voix",
+    "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."
 }

i18n/locale/it_IT.json

@@ -133,5 +133,10 @@
     "音调设置": "Impostazioni del tono",
     "音频设备": "Dispositivo audio",
     "音高算法": "音高算法",
-    "额外推理时长": "Tempo di inferenza extra"
+    "额外推理时长": "Tempo di inferenza extra",
+    "损失图": "Grafico delle perdite",
+    "选择语音": "Seleziona la voce",
+    "更新损失图": "Aggiornare il grafico delle perdite",
+    "更新语音列表": "Aggiornare l'elenco delle voci",
+    "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."
 }

i18n/locale/ja_JP.json

@@ -133,5 +133,10 @@
     "音调设置": "音程設定",
     "音频设备": "オーディオデバイス",
     "音高算法": "ピッチアルゴリズム",
-    "额外推理时长": "追加推論時間"
+    "额外推理时长": "追加推論時間",
+    "损失图": "損失グラフ",
+    "选择语音": "音声を選択",
+    "更新损失图": "損失グラフを更新する",
+    "更新语音列表": "音声リストを更新する",
+    "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。"
 }

i18n/locale/ko_KR.json

@@ -133,5 +133,10 @@
     "音调设置": "음조 설정",
     "音频设备": "音频设备",
     "音高算法": "음높이 알고리즘",
-    "额外推理时长": "추가 추론 시간"
+    "额外推理时长": "추가 추론 시간",
+    "损失图": "손실 그래프",
+    "选择语音": "음성 선택",
+    "更新损失图": "손실 그래프 업데이트",
+    "更新语音列表": "음성 목록 업데이트",
+    "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."
 }

i18n/locale/pt_BR.json

@@ -133,5 +133,10 @@
     "音调设置": "Configurações de tom",
     "音频设备": "音频设备",
     "音高算法": "Algoritmo de detecção de pitch",
-    "额外推理时长": "Tempo extra de inferência"
+    "额外推理时长": "Tempo extra de inferência",
+    "损失图": "Gráfico de perda",
+    "选择语音": "Selecione o áudio",
+    "更新损失图": "Atualizar gráfico de perda",
+    "更新语音列表": "Atualizar lista de áudio",
+    "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."
 }

i18n/locale/ru_RU.json

@@ -133,5 +133,10 @@
     "音调设置": "Настройка высоты звука",
     "音频设备": "Аудиоустройство",
     "音高算法": "Алгоритм оценки высоты звука",
-    "额外推理时长": "Доп. время переработки"
+    "额外推理时长": "Доп. время переработки",
+    "损失图": "График потерь",
+    "选择语音": "Выберите голос",
+    "更新损失图": "Обновить график потерь",
+    "更新语音列表": "Обновить список голосов",
+    "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."
 }

i18n/locale/tr_TR.json

@@ -133,5 +133,10 @@
     "音调设置": "Pitch ayarları",
     "音频设备": "Ses cihazı",
     "音高算法": "音高算法",
-    "额外推理时长": "Ekstra çıkartma süresi"
+    "额外推理时长": "Ekstra çıkartma süresi",
+    "损失图": "Kayıp grafiği",
+    "选择语音": "Konuşma seç",
+    "更新损失图": "Kayıp grafiğini güncelle",
+    "更新语音列表": "Konuşma listesini güncelle",
+    "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."
 }

i18n/locale/zh_CN.json

@@ -133,5 +133,10 @@
     "音调设置": "音调设置",
     "音频设备": "音频设备",
     "音高算法": "音高算法",
-    "额外推理时长": "额外推理时长"
+    "额外推理时长": "额外推理时长",
+    "损失图": "损失图",
+    "选择语音": "选择语音",
+    "更新损失图": "更新损失图",
+    "更新语音列表": "更新语音列表",
+    "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。"
 }

i18n/locale/zh_HK.json

@@ -133,5 +133,10 @@
     "音调设置": "音調設定",
     "音频设备": "音訊設備",
     "音高算法": "音高演算法",
-    "额外推理时长": "額外推理時長"
+    "额外推理时长": "額外推理時長",
+    "损失图": "損失圖",
+    "选择语音": "選擇語音",
+    "更新损失图": "更新損失圖",
+    "更新语音列表": "更新語音列表",
+    "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。"
 }

i18n/locale/zh_SG.json

@@ -133,5 +133,10 @@
     "音调设置": "音調設定",
     "音频设备": "音訊設備",
     "音高算法": "音高演算法",
-    "额外推理时长": "額外推理時長"
+    "额外推理时长": "額外推理時長",
+    "损失图": "損失圖",
+    "选择语音": "選擇語音",
+    "更新损失图": "更新損失圖",
+    "更新语音列表": "更新語音列表",
+    "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。"
 }

i18n/locale/zh_TW.json

@@ -133,5 +133,10 @@
     "音调设置": "音調設定",
     "音频设备": "音訊設備",
     "音高算法": "音高演算法",
-    "额外推理时长": "額外推理時長"
+    "额外推理时长": "額外推理時長",
+    "损失图": "損失圖",
+    "选择语音": "选择语音",
+    "更新损失图": "更新損失圖",
+    "更新语音列表": "更新语音列表",
+    "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。"
 }

infer-web.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import os
 import sys
 from dotenv import load_dotenv
@@ -806,6 +807,100 @@ def change_f0_method(f0method8):
     return {"visible": visible, "__type__": "update"}
 
 
+# start tab loss graph helper functions
+desired_tags = ["loss_d_total", "loss_g_total", "loss_g_fm", "loss_g_mel", "loss_g_kl"]
+
+def get_projects():
+    """
+    Get the list of projects.
+    """
+    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'))]
+
+def get_loss_graph_images(selection):
+    """
+    Gets loss graph images for a given project, assuming filenames match desired order.
+
+    Args:
+        selection (str): Project name.
+
+    Returns:
+        dict: A dictionary of image paths keyed by desired_tags.
+    """
+    loss_graphs_path = os.path.join(index_root, selection, 'loss_graphs')
+    if not os.path.exists(loss_graphs_path):
+        print(f"Directory not found: {loss_graphs_path}")
+        return {}
+
+    graphs = {}
+    for tag in desired_tags:
+        image_path = os.path.join(loss_graphs_path, f"{tag}.jpeg")
+        if os.path.exists(image_path):
+            graphs[tag] = image_path
+
+    return graphs
+
+def get_loss_graph_tabs(project):
+    """
+    Create Gradio Tabs and Image fields for the loss graphs.
+
+    Args:
+        project (str): Project name.
+
+    Returns:
+        gr.Tabs, list: A tuple containing the Gradio Tabs component and a list of image fields.
+    """
+    loss_graph_tabs = gr.Tabs()
+    loss_graph_images = get_loss_graph_images(project)
+    loss_graph_image_fields = {}
+
+    with loss_graph_tabs:
+        for tag, image_path in loss_graph_images.items():
+            with gr.TabItem(tag):
+                image_field = gr.Image(value=image_path, width="100%")
+                loss_graph_image_fields[tag] = image_field
+    return loss_graph_tabs, list(loss_graph_image_fields.values())
+
+def update_loss_graph_images(selection):
+    """
+    Update the loss graph images for a given project.
+    """
+    loss_graph_images = get_loss_graph_images(selection)
+    updated_values = []
+
+    for i, tag in enumerate(desired_tags):
+        if i < len(image_fields):
+            if tag in loss_graph_images:
+                image_path = loss_graph_images[tag]
+                if os.path.exists(image_path) and os.path.isfile(image_path):
+                    updated_values.append(image_path)
+                else:
+                    print(f"Warning: Image file does not exist or is not a file: {image_path}")
+                    updated_values.append(None)
+            else:
+                print(f"Warning: No image found for tag: {tag}")
+                updated_values.append(None)
+
+    return updated_values
+
+def update_projects():
+    """
+    Update the list of projects.
+    """
+    projects = get_projects()
+    return {"choices": sorted(projects), "__type__": "update"}
+
+projects = get_projects()
+
+# Check if there are any projects before accessing
+if projects:
+    default_project = projects[0]
+    default_loss_graph_images = get_loss_graph_images(projects[0])
+else:
+    print("No projects found.")
+    default_project = None
+    default_loss_graph_images = []
+
+# gradio app
 with gr.Blocks(title="RVC WebUI") as app:
     gr.Markdown("## RVC WebUI")
     gr.Markdown(
@@ -1420,7 +1515,48 @@ with gr.Blocks(title="RVC WebUI") as app:
                         info3,
                         api_name="train_start_all",
                     )
+        with gr.TabItem(i18n("损失图")):
+            gr.Markdown(
+                value=i18n(
+                    "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。"
+                )
+            )
+            with gr.Row():
+                voice_list_dropdown = gr.Dropdown(
+                    label=i18n("选择语音"), 
+                    choices=sorted(projects), 
+                    interactive=True, 
+                    value=default_project
+                )
+                with gr.Column():
+                    update_voice_list_button = gr.Button(
+                        i18n("更新语音列表"), 
+                        variant="primary"
+                    )
+                    update_loss_graph_button = gr.Button(
+                        i18n("更新损失图"), 
+                        variant="primary"
+                    )
+                update_voice_list_button.click(
+                    fn=update_projects, 
+                    inputs=[], 
+                    outputs=[voice_list_dropdown], 
+                    api_name="infer_refresh"
+                )
+            with gr.Row():
+                tabs, image_fields = get_loss_graph_tabs(default_project)
 
+                voice_list_dropdown.change(
+                    fn=update_loss_graph_images,
+                    inputs=voice_list_dropdown,
+                    outputs=image_fields
+                )
+
+                update_loss_graph_button.click(
+                    fn=update_loss_graph_images,
+                    inputs=voice_list_dropdown,
+                    outputs=image_fields
+                )
         with gr.TabItem(i18n("ckpt处理")):
             with gr.Group():
                 gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))

infer/lib/train/graph_generation.py

@@ -0,0 +1,128 @@
+import os
+from typing import List
+import matplotlib.pyplot as plt
+import logging
+
+# Suppress TensorBoard event processing logs
+logging.getLogger('tensorboard').setLevel(logging.ERROR)
+
+# Import TensorBoard modules after setting logging level
+from tensorboard.backend.event_processing import event_accumulator
+
+def generate_loss_graphs(log_dir):
+    """
+    Generates and saves plots for the given TensorBoard logs.
+    Args:
+        log_dir (str): Directory with TensorBoard logs.
+    """
+    scalar_data = extract_scalar_data(log_dir)
+    plot_scalar_data(scalar_data, log_dir)
+
+def smooth(scalars: List[float], weight: float) -> List[float]:
+    """
+    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]
+    smoothed = []
+    for point in scalars:
+        smoothed_val = last * weight + (1 - weight) * point
+        smoothed.append(smoothed_val)
+        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()

infer/modules/train/train.py

@@ -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