Ver 2.7.0 maybe

gradio_tabs/dataset.py

@@ -51,6 +51,11 @@ def do_transcribe(
 ):
     if model_name == "":
         return "Error: モデル名を入力してください。"
+    if hf_repo_id == "litagin/anime-whisper":
+        logger.info(
+            "Since litagin/anime-whisper does not support initial prompt, it will be ignored."
+        )
+        initial_prompt = ""
 
     cmd = [
         "transcribe.py",
@@ -159,34 +164,30 @@ def create_dataset_app() -> gr.Blocks:
                 result1 = gr.Textbox(label="結果")
         with gr.Row():
             with gr.Column():
+                use_hf_whisper = gr.Checkbox(
+                    label="HuggingFaceのWhisperを使う（速度が速いがVRAMを多く使う）",
+                    value=False,
+                )
                 whisper_model = gr.Dropdown(
                     [
-                        "tiny",
-                        "base",
-                        "small",
-                        "medium",
                         "large",
                         "large-v2",
                         "large-v3",
                     ],
                     label="Whisperモデル",
                     value="large-v3",
-                )
-                use_hf_whisper = gr.Checkbox(
-                    label="HuggingFaceのWhisperを使う（速度が速いがVRAMを多く使う）",
-                    value=False,
+                    visible=True,
                 )
                 hf_repo_id = gr.Dropdown(
                     [
                         "openai/whisper-large-v3-turbo",
                         "openai/whisper-large-v3",
                         "openai/whisper-large-v2",
-                        "kotoba-tech/kotoba-whisper-v1.1",
                         "kotoba-tech/kotoba-whisper-v2.1",
                         "litagin/anime-whisper",
                     ],
                     label="HuggingFaceのWhisper repo_id",
-                    value="openai/whisper-large-v3",
+                    value="openai/whisper-large-v3-turbo",
                     visible=False,
                 )
                 compute_type = gr.Dropdown(
@@ -258,12 +259,13 @@ def create_dataset_app() -> gr.Blocks:
         )
         use_hf_whisper.change(
             lambda x: (
+                gr.update(visible=not x),
                 gr.update(visible=x),
                 gr.update(visible=x),
                 gr.update(visible=not x),
             ),
             inputs=[use_hf_whisper],
-            outputs=[hf_repo_id, batch_size, compute_type],
+            outputs=[whisper_model, hf_repo_id, batch_size, compute_type],
         )
 
     return app