Merge pull request #79 from fakerybakery/patch-1

gradio_app.py

@@ -21,6 +21,20 @@ import librosa
 import click
 import soundfile as sf
 
+try:
+    import spaces
+    USING_SPACES = True
+except ImportError:
+    USING_SPACES = False
+
+def gpu_decorator(func):
+    if USING_SPACES:
+        return spaces.GPU(func)
+    else:
+        return func
+
+
+
 SPLIT_WORDS = [
     "but", "however", "nevertheless", "yet", "still",
     "therefore", "thus", "hence", "consequently",
@@ -406,24 +420,15 @@ def update_speed(new_speed):
     speed = new_speed
     return f"Speed set to: {speed}"
 
-with gr.Blocks() as app:
-    gr.Markdown(
-        """
-# E2/F5 TTS with Advanced Batch Processing
-
-This is a local web UI for F5 TTS with advanced batch processing support, based on the unofficial [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) supported by [mrfakename](https://github.com/fakerybakery). This app supports the following TTS models:
-
-* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
-* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
-
-The checkpoints support English and Chinese.
-
-If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt.
-
-**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
-"""
-    )
+with gr.Blocks() as app_credits:
+    gr.Markdown("""
+# Credits
 
+* [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
+* [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation
+""")
+with gr.Blocks() as app_tts:
+    gr.Markdown("# Batched TTS")
     ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
     gen_text_input = gr.Textbox(label="Text to Generate", lines=10)
     model_choice = gr.Radio(
@@ -472,38 +477,32 @@ If you're having issues, try converting your reference audio to WAV or MP3, clip
         outputs=[audio_output, spectrogram_output],
     )
     
-    gr.Markdown(
-        """
-# Podcast Generation
-
-Supported by [RootingInLoad](https://github.com/RootingInLoad)
-"""
-    )
-    with gr.Tab("Podcast Generation"):
-        speaker1_name = gr.Textbox(label="Speaker 1 Name")
-        ref_audio_input1 = gr.Audio(label="Reference Audio (Speaker 1)", type="filepath")
-        ref_text_input1 = gr.Textbox(label="Reference Text (Speaker 1)", lines=2)
-        
-        speaker2_name = gr.Textbox(label="Speaker 2 Name")
-        ref_audio_input2 = gr.Audio(label="Reference Audio (Speaker 2)", type="filepath")
-        ref_text_input2 = gr.Textbox(label="Reference Text (Speaker 2)", lines=2)
-        
-        script_input = gr.Textbox(label="Podcast Script", lines=10, 
-                                  placeholder="Enter the script with speaker names at the start of each block, e.g.:\nSean: How did you start studying...\n\nMeghan: I came to my interest in technology...\nIt was a long journey...\n\nSean: That's fascinating. Can you elaborate...")
-        
-        podcast_model_choice = gr.Radio(
-            choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
-        )
-        podcast_remove_silence = gr.Checkbox(
-            label="Remove Silences",
-            value=True,
-        )
-        generate_podcast_btn = gr.Button("Generate Podcast", variant="primary")
-        podcast_output = gr.Audio(label="Generated Podcast")
+with gr.Blocks() as app_podcast:
+    gr.Markdown("# Podcast Generation")
+    speaker1_name = gr.Textbox(label="Speaker 1 Name")
+    ref_audio_input1 = gr.Audio(label="Reference Audio (Speaker 1)", type="filepath")
+    ref_text_input1 = gr.Textbox(label="Reference Text (Speaker 1)", lines=2)
     
+    speaker2_name = gr.Textbox(label="Speaker 2 Name")
+    ref_audio_input2 = gr.Audio(label="Reference Audio (Speaker 2)", type="filepath")
+    ref_text_input2 = gr.Textbox(label="Reference Text (Speaker 2)", lines=2)
+    
+    script_input = gr.Textbox(label="Podcast Script", lines=10, 
+                                placeholder="Enter the script with speaker names at the start of each block, e.g.:\nSean: How did you start studying...\n\nMeghan: I came to my interest in technology...\nIt was a long journey...\n\nSean: That's fascinating. Can you elaborate...")
+    
+    podcast_model_choice = gr.Radio(
+        choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
+    )
+    podcast_remove_silence = gr.Checkbox(
+        label="Remove Silences",
+        value=True,
+    )
+    generate_podcast_btn = gr.Button("Generate Podcast", variant="primary")
+    podcast_output = gr.Audio(label="Generated Podcast")
+
     def podcast_generation(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence):
         return generate_podcast(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence)
-    
+
     generate_podcast_btn.click(
         podcast_generation,
         inputs=[
@@ -520,30 +519,31 @@ Supported by [RootingInLoad](https://github.com/RootingInLoad)
         outputs=podcast_output,
     )
 
-    def parse_emotional_text(gen_text):
-        # Pattern to find (Emotion)
-        pattern = r'\((.*?)\)'
+def parse_emotional_text(gen_text):
+    # Pattern to find (Emotion)
+    pattern = r'\((.*?)\)'
 
-        # Split the text by the pattern
-        tokens = re.split(pattern, gen_text)
+    # Split the text by the pattern
+    tokens = re.split(pattern, gen_text)
 
-        segments = []
+    segments = []
 
-        current_emotion = 'Regular'
+    current_emotion = 'Regular'
 
-        for i in range(len(tokens)):
-            if i % 2 == 0:
-                # This is text
-                text = tokens[i].strip()
-                if text:
-                    segments.append({'emotion': current_emotion, 'text': text})
-            else:
-                # This is emotion
-                emotion = tokens[i].strip()
-                current_emotion = emotion
+    for i in range(len(tokens)):
+        if i % 2 == 0:
+            # This is text
+            text = tokens[i].strip()
+            if text:
+                segments.append({'emotion': current_emotion, 'text': text})
+        else:
+            # This is emotion
+            emotion = tokens[i].strip()
+            current_emotion = emotion
 
-        return segments
+    return segments
 
+with gr.Blocks() as app_emotional:
     # New section for emotional generation
     gr.Markdown(
         """
@@ -557,233 +557,250 @@ Supported by [RootingInLoad](https://github.com/RootingInLoad)
     """
     )
 
-    with gr.Tab("Multiple Speech-Type Generation"):
-        gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.")
+    gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.")
+
+    # Regular speech type (mandatory)
+    with gr.Row():
+        regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False)
+        regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath')
+        regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2)
+
+    # Additional speech types (up to 9 more)
+    max_speech_types = 10
+    speech_type_names = []
+    speech_type_audios = []
+    speech_type_ref_texts = []
+    speech_type_delete_btns = []
 
-        # Regular speech type (mandatory)
+    for i in range(max_speech_types - 1):
         with gr.Row():
-            regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False)
-            regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath')
-            regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2)
-
-        # Additional speech types (up to 9 more)
-        max_speech_types = 10
-        speech_type_names = []
-        speech_type_audios = []
-        speech_type_ref_texts = []
-        speech_type_delete_btns = []
-
-        for i in range(max_speech_types - 1):
-            with gr.Row():
-                name_input = gr.Textbox(label='Speech Type Name', visible=False)
-                audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False)
-                ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False)
-                delete_btn = gr.Button("Delete", variant="secondary", visible=False)
-            speech_type_names.append(name_input)
-            speech_type_audios.append(audio_input)
-            speech_type_ref_texts.append(ref_text_input)
-            speech_type_delete_btns.append(delete_btn)
-
-        # Button to add speech type
-        add_speech_type_btn = gr.Button("Add Speech Type")
-
-        # Keep track of current number of speech types
-        speech_type_count = gr.State(value=0)
-
-        # Function to add a speech type
-        def add_speech_type_fn(speech_type_count):
-            if speech_type_count < max_speech_types - 1:
-                speech_type_count += 1
-                # Prepare updates for the components
-                name_updates = []
-                audio_updates = []
-                ref_text_updates = []
-                delete_btn_updates = []
-                for i in range(max_speech_types - 1):
-                    if i < speech_type_count:
-                        name_updates.append(gr.update(visible=True))
-                        audio_updates.append(gr.update(visible=True))
-                        ref_text_updates.append(gr.update(visible=True))
-                        delete_btn_updates.append(gr.update(visible=True))
-                    else:
-                        name_updates.append(gr.update())
-                        audio_updates.append(gr.update())
-                        ref_text_updates.append(gr.update())
-                        delete_btn_updates.append(gr.update())
-            else:
-                # Optionally, show a warning
-                # gr.Warning("Maximum number of speech types reached.")
-                name_updates = [gr.update() for _ in range(max_speech_types - 1)]
-                audio_updates = [gr.update() for _ in range(max_speech_types - 1)]
-                ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)]
-                delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            name_input = gr.Textbox(label='Speech Type Name', visible=False)
+            audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False)
+            ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False)
+            delete_btn = gr.Button("Delete", variant="secondary", visible=False)
+        speech_type_names.append(name_input)
+        speech_type_audios.append(audio_input)
+        speech_type_ref_texts.append(ref_text_input)
+        speech_type_delete_btns.append(delete_btn)
+
+    # Button to add speech type
+    add_speech_type_btn = gr.Button("Add Speech Type")
+
+    # Keep track of current number of speech types
+    speech_type_count = gr.State(value=0)
+
+    # Function to add a speech type
+    def add_speech_type_fn(speech_type_count):
+        if speech_type_count < max_speech_types - 1:
+            speech_type_count += 1
+            # Prepare updates for the components
+            name_updates = []
+            audio_updates = []
+            ref_text_updates = []
+            delete_btn_updates = []
+            for i in range(max_speech_types - 1):
+                if i < speech_type_count:
+                    name_updates.append(gr.update(visible=True))
+                    audio_updates.append(gr.update(visible=True))
+                    ref_text_updates.append(gr.update(visible=True))
+                    delete_btn_updates.append(gr.update(visible=True))
+                else:
+                    name_updates.append(gr.update())
+                    audio_updates.append(gr.update())
+                    ref_text_updates.append(gr.update())
+                    delete_btn_updates.append(gr.update())
+        else:
+            # Optionally, show a warning
+            # gr.Warning("Maximum number of speech types reached.")
+            name_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            audio_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)]
+        return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
+
+    add_speech_type_btn.click(
+        add_speech_type_fn,
+        inputs=speech_type_count,
+        outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
+    )
+
+    # Function to delete a speech type
+    def make_delete_speech_type_fn(index):
+        def delete_speech_type_fn(speech_type_count):
+            # Prepare updates
+            name_updates = []
+            audio_updates = []
+            ref_text_updates = []
+            delete_btn_updates = []
+
+            for i in range(max_speech_types - 1):
+                if i == index:
+                    name_updates.append(gr.update(visible=False, value=''))
+                    audio_updates.append(gr.update(visible=False, value=None))
+                    ref_text_updates.append(gr.update(visible=False, value=''))
+                    delete_btn_updates.append(gr.update(visible=False))
+                else:
+                    name_updates.append(gr.update())
+                    audio_updates.append(gr.update())
+                    ref_text_updates.append(gr.update())
+                    delete_btn_updates.append(gr.update())
+
+            speech_type_count = max(0, speech_type_count - 1)
+
             return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
 
-        add_speech_type_btn.click(
-            add_speech_type_fn,
+        return delete_speech_type_fn
+
+    for i, delete_btn in enumerate(speech_type_delete_btns):
+        delete_fn = make_delete_speech_type_fn(i)
+        delete_btn.click(
+            delete_fn,
             inputs=speech_type_count,
             outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
         )
 
-        # Function to delete a speech type
-        def make_delete_speech_type_fn(index):
-            def delete_speech_type_fn(speech_type_count):
-                # Prepare updates
-                name_updates = []
-                audio_updates = []
-                ref_text_updates = []
-                delete_btn_updates = []
-
-                for i in range(max_speech_types - 1):
-                    if i == index:
-                        name_updates.append(gr.update(visible=False, value=''))
-                        audio_updates.append(gr.update(visible=False, value=None))
-                        ref_text_updates.append(gr.update(visible=False, value=''))
-                        delete_btn_updates.append(gr.update(visible=False))
-                    else:
-                        name_updates.append(gr.update())
-                        audio_updates.append(gr.update())
-                        ref_text_updates.append(gr.update())
-                        delete_btn_updates.append(gr.update())
+    # Text input for the prompt
+    gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10)
 
-                speech_type_count = max(0, speech_type_count - 1)
+    # Model choice
+    model_choice_emotional = gr.Radio(
+        choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
+    )
 
-                return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
+    with gr.Accordion("Advanced Settings", open=False):
+        remove_silence_emotional = gr.Checkbox(
+            label="Remove Silences",
+            value=True,
+        )
 
-            return delete_speech_type_fn
+    # Generate button
+    generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary")
+
+    # Output audio
+    audio_output_emotional = gr.Audio(label="Synthesized Audio")
+
+    def generate_emotional_speech(
+        regular_audio,
+        regular_ref_text,
+        gen_text,
+        *args,
+    ):
+        num_additional_speech_types = max_speech_types - 1
+        speech_type_names_list = args[:num_additional_speech_types]
+        speech_type_audios_list = args[num_additional_speech_types:2 * num_additional_speech_types]
+        speech_type_ref_texts_list = args[2 * num_additional_speech_types:3 * num_additional_speech_types]
+        model_choice = args[3 * num_additional_speech_types]
+        remove_silence = args[3 * num_additional_speech_types + 1]
+
+        # Collect the speech types and their audios into a dict
+        speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}}
+
+        for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list):
+            if name_input and audio_input:
+                speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input}
+
+        # Parse the gen_text into segments
+        segments = parse_speechtypes_text(gen_text)
+
+        # For each segment, generate speech
+        generated_audio_segments = []
+        current_emotion = 'Regular'
 
-        for i, delete_btn in enumerate(speech_type_delete_btns):
-            delete_fn = make_delete_speech_type_fn(i)
-            delete_btn.click(
-                delete_fn,
-                inputs=speech_type_count,
-                outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
-            )
+        for segment in segments:
+            emotion = segment['emotion']
+            text = segment['text']
 
-        # Text input for the prompt
-        gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10)
+            if emotion in speech_types:
+                current_emotion = emotion
+            else:
+                # If emotion not available, default to Regular
+                current_emotion = 'Regular'
 
-        # Model choice
-        model_choice_emotional = gr.Radio(
-            choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
-        )
+            ref_audio = speech_types[current_emotion]['audio']
+            ref_text = speech_types[current_emotion].get('ref_text', '')
 
-        with gr.Accordion("Advanced Settings", open=False):
-            remove_silence_emotional = gr.Checkbox(
-                label="Remove Silences",
-                value=True,
-            )
+            # Generate speech for this segment
+            audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, "")
+            sr, audio_data = audio
 
-        # Generate button
-        generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary")
+            generated_audio_segments.append(audio_data)
 
-        # Output audio
-        audio_output_emotional = gr.Audio(label="Synthesized Audio")
+        # Concatenate all audio segments
+        if generated_audio_segments:
+            final_audio_data = np.concatenate(generated_audio_segments)
+            return (sr, final_audio_data)
+        else:
+            gr.Warning("No audio generated.")
+            return None
 
-        def generate_emotional_speech(
+    generate_emotional_btn.click(
+        generate_emotional_speech,
+        inputs=[
             regular_audio,
             regular_ref_text,
-            gen_text,
-            *args,
-        ):
-            num_additional_speech_types = max_speech_types - 1
-            speech_type_names_list = args[:num_additional_speech_types]
-            speech_type_audios_list = args[num_additional_speech_types:2 * num_additional_speech_types]
-            speech_type_ref_texts_list = args[2 * num_additional_speech_types:3 * num_additional_speech_types]
-            model_choice = args[3 * num_additional_speech_types]
-            remove_silence = args[3 * num_additional_speech_types + 1]
-
-            # Collect the speech types and their audios into a dict
-            speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}}
-
-            for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list):
-                if name_input and audio_input:
-                    speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input}
-
-            # Parse the gen_text into segments
-            segments = parse_speechtypes_text(gen_text)
-
-            # For each segment, generate speech
-            generated_audio_segments = []
-            current_emotion = 'Regular'
-
-            for segment in segments:
-                emotion = segment['emotion']
-                text = segment['text']
-
-                if emotion in speech_types:
-                    current_emotion = emotion
-                else:
-                    # If emotion not available, default to Regular
-                    current_emotion = 'Regular'
+            gen_text_input_emotional,
+        ] + speech_type_names + speech_type_audios + speech_type_ref_texts + [
+            model_choice_emotional,
+            remove_silence_emotional,
+        ],
+        outputs=audio_output_emotional,
+    )
 
-                ref_audio = speech_types[current_emotion]['audio']
-                ref_text = speech_types[current_emotion].get('ref_text', '')
+    # Validation function to disable Generate button if speech types are missing
+    def validate_speech_types(
+        gen_text,
+        regular_name,
+        *args
+    ):
+        num_additional_speech_types = max_speech_types - 1
+        speech_type_names_list = args[:num_additional_speech_types]
+
+        # Collect the speech types names
+        speech_types_available = set()
+        if regular_name:
+            speech_types_available.add(regular_name)
+        for name_input in speech_type_names_list:
+            if name_input:
+                speech_types_available.add(name_input)
+
+        # Parse the gen_text to get the speech types used
+        segments = parse_emotional_text(gen_text)
+        speech_types_in_text = set(segment['emotion'] for segment in segments)
+
+        # Check if all speech types in text are available
+        missing_speech_types = speech_types_in_text - speech_types_available
+
+        if missing_speech_types:
+            # Disable the generate button
+            return gr.update(interactive=False)
+        else:
+            # Enable the generate button
+            return gr.update(interactive=True)
 
-                # Generate speech for this segment
-                audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, "")
-                sr, audio_data = audio
+    gen_text_input_emotional.change(
+        validate_speech_types,
+        inputs=[gen_text_input_emotional, regular_name] + speech_type_names,
+        outputs=generate_emotional_btn
+    )
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+# E2/F5 TTS
 
-                generated_audio_segments.append(audio_data)
+This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:
 
-            # Concatenate all audio segments
-            if generated_audio_segments:
-                final_audio_data = np.concatenate(generated_audio_segments)
-                return (sr, final_audio_data)
-            else:
-                gr.Warning("No audio generated.")
-                return None
-
-        generate_emotional_btn.click(
-            generate_emotional_speech,
-            inputs=[
-                regular_audio,
-                regular_ref_text,
-                gen_text_input_emotional,
-            ] + speech_type_names + speech_type_audios + speech_type_ref_texts + [
-                model_choice_emotional,
-                remove_silence_emotional,
-            ],
-            outputs=audio_output_emotional,
-        )
+* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
+* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
 
-        # Validation function to disable Generate button if speech types are missing
-        def validate_speech_types(
-            gen_text,
-            regular_name,
-            *args
-        ):
-            num_additional_speech_types = max_speech_types - 1
-            speech_type_names_list = args[:num_additional_speech_types]
-
-            # Collect the speech types names
-            speech_types_available = set()
-            if regular_name:
-                speech_types_available.add(regular_name)
-            for name_input in speech_type_names_list:
-                if name_input:
-                    speech_types_available.add(name_input)
-
-            # Parse the gen_text to get the speech types used
-            segments = parse_emotional_text(gen_text)
-            speech_types_in_text = set(segment['emotion'] for segment in segments)
-
-            # Check if all speech types in text are available
-            missing_speech_types = speech_types_in_text - speech_types_available
-
-            if missing_speech_types:
-                # Disable the generate button
-                return gr.update(interactive=False)
-            else:
-                # Enable the generate button
-                return gr.update(interactive=True)
+The checkpoints support English and Chinese.
+
+If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt.
+
+**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
+"""
+    )
+    gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "Multi-Style", "Credits"])
 
-        gen_text_input_emotional.change(
-            validate_speech_types,
-            inputs=[gen_text_input_emotional, regular_name] + speech_type_names,
-            outputs=generate_emotional_btn
-        )
-    
 @click.command()
 @click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
 @click.option("--host", "-H", default=None, help="Host to run the app on")
@@ -804,4 +821,4 @@ def main(port, host, share, api):
 
 
 if __name__ == "__main__":
-    main()
+    main()