f5tts - still a work in progress
Just uploading this so I have a reference point in time that I wont lose.
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{
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"first_start_model": "f5tts_v1",
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"models": [
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{
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"model_name": "f5tts_v1",
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"folder_path": "f5tts_v1",
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"files_to_download": {
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"model_1200000.safetensors": "https://huggingface.co/SWivid/F5-TTS/resolve/main/F5TTS_Base/model_1200000.safetensors",
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"vocab.txt": "https://raw.githubusercontent.com/SWivid/F5-TTS/refs/heads/main/src/f5_tts/infer/examples/vocab.txt",
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"vocos/config.yaml": "https://huggingface.co/charactr/vocos-mel-24khz/resolve/main/config.yaml",
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"vocos/pytorch_model.bin": "https://huggingface.co/charactr/vocos-mel-24khz/resolve/main/pytorch_model.bin"
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}
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}
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]
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}
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import os
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import json
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import requests
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import gradio as gr
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from tqdm import tqdm
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from pathlib import Path
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this_dir = Path(__file__).parent.resolve() # Sets up self.this_dir as a variable for the folder THIS script is running in.
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main_dir = Path(__file__).parent.parent.parent.parent.resolve() # Sets up self.main_dir as a variable for the folder AllTalk is running in
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##########################################################################
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# REQUIRED CHANGE #
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# Populate the voices list, using the method specific to your TTS engine #
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##########################################################################
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# This function is responsible for populating the list of available voices for your TTS engine.
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# You need to modify this function to use the appropriate method for your engine to retrieve the voice list.
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#
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# The current implementation lists all the WAV files in a "voices" directory, which may not be suitable for your engine.
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# You should replace the `xxxx_voices_file_list` function name to match your engine name. For example, if your engine
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# is named "mytts", the function should be named `mytts_voices_file_list`.
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#
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# You will also neef to update the code with your own implementation that retrieves the voice list according to your
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# engine's specific requirements. Typically this is the same code as will be in your model_engine.py file.
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#
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# For example, if your engine has a dedicated API or configuration file for managing voices, you should modify this
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# function to interact with that API or read from that configuration file.
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#
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# After making the necessary changes, this function should return a list of available voices that can be used
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# in your TTS engine's settings page.
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def f5tts_voices_file_list():
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directory = main_dir / "voices"
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voices = []
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def has_reference_text(wav_path):
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text_path = wav_path.with_suffix('.reference.txt')
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return text_path.exists()
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# Check files in main voices directory
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for f in directory.glob("*.wav"):
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if has_reference_text(f):
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voices.append(f.name)
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# Check subdirectories
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for folder in directory.iterdir():
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if folder.is_dir():
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for wav_file in folder.glob("*.wav"):
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if has_reference_text(wav_file):
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# If it's in a subdirectory, add the subdirectory name
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rel_path = wav_file.relative_to(directory)
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voices.append(str(rel_path))
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if not voices:
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return ["No Voices Found"]
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return sorted(voices)
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######################################################
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# REQUIRED CHANGE #
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# Imports and saves the TTS engine-specific settings #
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######################################################
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# This function is responsible for importing and saving the settings specific to your TTS engine.
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# You need to make the following change:
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#
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# 1. Change the name of the function `xxxx_model_update_settings` to match your engine's name.
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# For example, if your engine is named "mytts", the function should be named `mytts_model_update_settings`.
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#
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# After making this change, the function will load the model settings from a JSON file, update the settings and voice
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# dictionaries with the values provided as arguments, and save the updated settings back to the JSON file.
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#
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# You do not need to modify the function's logic or any other part of the code.
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def f5tts_model_update_settings(def_character_voice_gr, def_narrator_voice_gr, lowvram_enabled_gr, deepspeed_enabled_gr, temperature_set_gr, repetitionpenalty_set_gr, pitch_set_gr, generationspeed_set_gr, alloy_gr, echo_gr, fable_gr, nova_gr, onyx_gr, shimmer_gr):
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# Load the model_config_data from the JSON file
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with open(os.path.join(this_dir, "model_settings.json"), "r") as f:
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model_config_data = json.load(f)
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# Update the settings and openai_voices dictionaries with the new values
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model_config_data["settings"]["def_character_voice"] = def_character_voice_gr
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model_config_data["settings"]["def_narrator_voice"] = def_narrator_voice_gr
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model_config_data["openai_voices"]["alloy"] = alloy_gr
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model_config_data["openai_voices"]["echo"] = echo_gr
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model_config_data["openai_voices"]["fable"] = fable_gr
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model_config_data["openai_voices"]["nova"] = nova_gr
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model_config_data["openai_voices"]["onyx"] = onyx_gr
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model_config_data["openai_voices"]["shimmer"] = shimmer_gr
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model_config_data["settings"]["lowvram_enabled"] = lowvram_enabled_gr == "Enabled"
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model_config_data["settings"]["deepspeed_enabled"] = deepspeed_enabled_gr == "Enabled"
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model_config_data["settings"]["temperature_set"] = temperature_set_gr
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model_config_data["settings"]["repetitionpenalty_set"] = repetitionpenalty_set_gr
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model_config_data["settings"]["pitch_set"] = pitch_set_gr
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model_config_data["settings"]["generationspeed_set"] = generationspeed_set_gr
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# Save the updated model_config_data to the JSON file
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with open(os.path.join(this_dir, "model_settings.json"), "w") as f:
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json.dump(model_config_data, f, indent=4)
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return "Settings updated successfully!"
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#######################################################
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# REQUIRED CHANGE #
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# Sets up the engine-specific settings page in Gradio #
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#######################################################
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# This function sets up the Gradio interface for the settings page specific to your TTS engine.
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# You need to make the following changes:
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#
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# 1. Change the name of the function `xxxx_model_alltalk_settings` to match your engine's name.
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# For example, if your engine is named "mytts", the function should be named `mytts_model_alltalk_settings`.
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#
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# 2. Change the name of the `submit_button.click` function call to match the name you gave to the function
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# that imports and saves your engine's settings (the function you modified above).
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#
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# 3. Change the name of the `voice_list` function call to match the name of the function that lists
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# the available voices for your TTS engine.
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#
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# 4. Change the 'title' of the `gr.Blocks` to match your engine's name e.g. title="mytts TTS"
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#
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# After making these changes, this function will create and return the Gradio interface for your TTS engine's
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# settings page, allowing users to configure various options and voice selections.
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def f5tts_model_alltalk_settings(model_config_data):
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features_list = model_config_data['model_capabilties']
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voice_list = f5tts_voices_file_list()
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with gr.Blocks(title="f5tts TTS", analytics_enabled=False) as app:
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with gr.Tab("Default Settings"):
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with gr.Row():
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lowvram_enabled_gr = gr.Radio(choices={"Enabled": "true", "Disabled": "false"}, label="Low VRAM" if model_config_data["model_capabilties"]["lowvram_capable"] else "Low VRAM N/A", value="Enabled" if model_config_data["settings"]["lowvram_enabled"] else "Disabled", interactive=model_config_data["model_capabilties"]["lowvram_capable"])
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deepspeed_enabled_gr = gr.Radio(choices={"Enabled": "true", "Disabled": "false"}, label="DeepSpeed Activate" if model_config_data["model_capabilties"]["deepspeed_capable"] else "DeepSpeed N/A", value="Enabled" if model_config_data["settings"]["deepspeed_enabled"] else "Disabled", interactive=model_config_data["model_capabilties"]["deepspeed_capable"])
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temperature_set_gr = gr.Slider(value=float(model_config_data["settings"]["temperature_set"]), minimum=0, maximum=1, step=0.05, label="Temperature" if model_config_data["model_capabilties"]["temperature_capable"] else "Temperature N/A", interactive=model_config_data["model_capabilties"]["temperature_capable"])
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repetitionpenalty_set_gr = gr.Slider(value=float(model_config_data["settings"]["repetitionpenalty_set"]), minimum=1, maximum=20, step=1, label="Repetition Penalty" if model_config_data["model_capabilties"]["repetitionpenalty_capable"] else "Repetition N/A", interactive=model_config_data["model_capabilties"]["repetitionpenalty_capable"])
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pitch_set_gr = gr.Slider(value=float(model_config_data["settings"]["pitch_set"]), minimum=-10, maximum=10, step=1, label="Pitch" if model_config_data["model_capabilties"]["pitch_capable"] else "Pitch N/A", interactive=model_config_data["model_capabilties"]["pitch_capable"])
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generationspeed_set_gr = gr.Slider(value=float(model_config_data["settings"]["generationspeed_set"]), minimum=0.25, maximum=2.00, step=0.25, label="Speed" if model_config_data["model_capabilties"]["generationspeed_capable"] else "Speed N/A", interactive=model_config_data["model_capabilties"]["generationspeed_capable"])
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with gr.Row():
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with gr.Column():
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gr.Markdown("### OpenAI Voice Mappings")
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with gr.Group():
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with gr.Row():
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alloy_gr = gr.Dropdown(value=model_config_data["openai_voices"]["alloy"], label="Alloy", choices=voice_list, allow_custom_value=True)
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echo_gr = gr.Dropdown(value=model_config_data["openai_voices"]["echo"], label="Echo", choices=voice_list, allow_custom_value=True)
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with gr.Row():
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fable_gr = gr.Dropdown(value=model_config_data["openai_voices"]["fable"], label="Fable", choices=voice_list, allow_custom_value=True)
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nova_gr = gr.Dropdown(value=model_config_data["openai_voices"]["nova"], label="Nova", choices=voice_list, allow_custom_value=True)
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with gr.Row():
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onyx_gr = gr.Dropdown(value=model_config_data["openai_voices"]["onyx"], label="Onyx", choices=voice_list, allow_custom_value=True)
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shimmer_gr = gr.Dropdown(value=model_config_data["openai_voices"]["shimmer"], label="Shimmer", choices=voice_list, allow_custom_value=True)
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with gr.Column():
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gr.Markdown("### Default Voices")
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with gr.Row():
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def_character_voice_gr = gr.Dropdown(value=model_config_data["settings"]["def_character_voice"], label="Default/Character Voice", choices=voice_list, allow_custom_value=True)
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def_narrator_voice_gr = gr.Dropdown(value=model_config_data["settings"]["def_narrator_voice"], label="Narrator Voice", choices=voice_list, allow_custom_value=True)
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with gr.Group():
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with gr.Row():
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details_text = gr.Textbox(label="Details", show_label=False, lines=5, interactive=False, value="In this section, you can set the default settings for this TTS engine. Settings that are not supported by the current engine will be greyed out and cannot be selected. Default voices specified here will be used when no specific voice is provided in the TTS generation request. If a voice is specified in the request, it will override these default settings. When using the OpenAI API compatable API with this TTS engine, the voice mappings will be applied. As the OpenAI API has a limited set of 6 voices, these mappings ensure compatibility by mapping the OpenAI voices to the available voices in this TTS engine.")
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with gr.Row():
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submit_button = gr.Button("Update Settings")
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output_message = gr.Textbox(label="Output Message", interactive=False, show_label=False)
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submit_button.click(f5tts_model_update_settings, inputs=[def_character_voice_gr, def_narrator_voice_gr, lowvram_enabled_gr, deepspeed_enabled_gr, temperature_set_gr, repetitionpenalty_set_gr, pitch_set_gr, generationspeed_set_gr, alloy_gr, echo_gr, fable_gr, nova_gr, onyx_gr, shimmer_gr], outputs=output_message)
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###########################################################################################
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# Do not change this section apart from "TTS Engine Name" value to match your engine name #
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###########################################################################################
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with gr.Tab("Engine Information"):
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with gr.Row():
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with gr.Group():
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gr.Textbox(label="Manufacturer Name", value=model_config_data['model_details']['manufacturer_name'], interactive=False)
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gr.Textbox(label="Manufacturer Website/TTS Engine Support", value=model_config_data['model_details']['manufacturer_website'], interactive=False)
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gr.Textbox(label="Engine/Model Description", value=model_config_data['model_details']['model_description'], interactive=False, lines=13)
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with gr.Column():
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with gr.Row():
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gr.Textbox(label="DeepSpeed Capable", value='Yes' if features_list['deepspeed_capable'] else 'No', interactive=False)
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gr.Textbox(label="Pitch Capable", value='Yes' if features_list['pitch_capable'] else 'No', interactive=False)
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gr.Textbox(label="Generation Speed Capable", value='Yes' if features_list['generationspeed_capable'] else 'No', interactive=False)
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with gr.Row():
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gr.Textbox(label="Repetition Penalty Capable", value='Yes' if features_list['repetitionpenalty_capable'] else 'No', interactive=False)
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gr.Textbox(label="Multi Languages Capable", value='Yes' if features_list['languages_capable'] else 'No', interactive=False)
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gr.Textbox(label="Streaming Capable", value='Yes' if features_list['streaming_capable'] else 'No', interactive=False)
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with gr.Row():
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gr.Textbox(label="Low VRAM Capable", value='Yes' if features_list['lowvram_capable'] else 'No', interactive=False)
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gr.Textbox(label="Temperature Capable", value='Yes' if features_list['temperature_capable'] else 'No', interactive=False)
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gr.Textbox(label="Multi Model Capable Engine", value='Yes' if features_list['multimodel_capable'] else 'No', interactive=False)
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with gr.Row():
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gr.Textbox(label="Multi Voice Capable Models", value='Yes' if features_list['multivoice_capable'] else 'No', interactive=False)
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gr.Textbox(label="Default Audio output format", value=model_config_data['model_capabilties']['audio_format'], interactive=False)
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gr.Textbox(label="TTS Engine Name", value="f5tts", interactive=False)
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with gr.Row():
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gr.Textbox(label="Windows Support", value='Yes' if features_list['windows_capable'] else 'No', interactive=False)
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gr.Textbox(label="Linux Support", value='Yes' if features_list['linux_capable'] else 'No', interactive=False)
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gr.Textbox(label="Mac Support", value='Yes' if features_list['mac_capable'] else 'No', interactive=False)
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with gr.Row():
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gr.Markdown("""
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#### 🟧 DeepSpeed Capable
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DeepSpeed is a deep learning optimization library that can significantly speed up model training and inference. If a model is DeepSpeed capable, it means it can utilize DeepSpeed to accelerate the generation of text-to-speech output. This requires the model to be loaded into CUDA/VRAM on an Nvidia GPU and the model's inference method to support DeepSpeed.
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#### 🟧 Pitch Capable
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Pitch refers to the highness or lowness of a sound. If a model is pitch capable, it can adjust the pitch of the generated speech, allowing for more expressive and varied output.
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#### 🟧 Generation Speed Capable
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Generation speed refers to the rate at which the model can generate text-to-speech output. If a model is generation speed capable, it means the speed of the generated speech can be adjusted, making it faster or slower depending on the desired output.
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#### 🟧 Repetition Penalty Capable
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Repetition penalty is a technique used to discourage the model from repeating the same words or phrases multiple times in the same sounding way. If a model is repetition penalty capable, it can apply this penalty during generation to improve the diversity and naturalness of the output.
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#### 🟧 Multi-Languages Capable
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Multi-language capable models can generate speech in multiple languages. This means that the model has been trained on data from different languages and can switch between them during generation. Some models are language-specific.
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#### 🟧 Multi-Voice Capable
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Multi-voice capable models generate speech in multiple voices or speaking styles. This means that the model has been trained on data from different speakers and can mimic their voices during generation, or is a voice cloning model that can generate speech based on the input sample.
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""")
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gr.Markdown("""
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#### 🟧 Streaming Capable
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Streaming refers to the ability to generate speech output in real-time, without the need to generate the entire output before playback. If a model is streaming capable, it can generate speech on-the-fly, allowing for faster response times and more interactive applications.
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#### 🟧 Low VRAM Capable
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VRAM (Video Random Access Memory) is a type of memory used by GPUs to store and process data. If a model is low VRAM capable, it can efficiently utilize the available VRAM by moving data between CPU and GPU memory as needed, allowing for generation even on systems with limited VRAM where it may be competing with an LLM model for VRAM.
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#### 🟧 Temperature Capable
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Temperature is a hyperparameter that controls the randomness of the generated output. If a model is temperature capable, the temperature can be adjusted to make the output more or less random, affecting the creativity and variability of the generated speech.
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#### 🟧 Multi-Model Capable Engine
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If an engine is multi-model capable, it means that it can support and utilize multiple models for text-to-speech generation. This allows for greater flexibility and the ability to switch between different models depending on the desired output. Different models may be capable of different languages, specific languages, voices, etc.
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#### 🟧 Default Audio Output Format
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Specifies the file format in which the generated speech will be saved. Common audio formats include WAV, MP3, FLAC, Opus, AAC, and PCM. If you want different outputs, you can set the transcode function to change the output audio, though transcoding will add a little time to the generation and is not available for streaming generation.
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#### 🟧 Windows/Linux/Mac Support
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These indicators show whether the model and engine are compatible with Windows, Linux, or macOS. However, additional setup or requirements may be necessary to ensure full functionality on your operating system. Please note that full platform support has not been extensively tested.
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""")
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#######################################################################################################################################################################################################
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# REQUIRED CHANGE #
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# You will need to build a custom method to identify if your models are installed and download them. Store your models list in the available_models.json file, alongside your model_settings.json etc #
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#######################################################################################################################################################################################################
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with gr.Tab("Models/Voices Download"):
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with gr.Row():
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# Load the available models from the JSON file
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with open(os.path.join(this_dir, "available_models.json"), "r") as f:
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available_models = json.load(f)
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# Extract the model names for the dropdown
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model_names = [model["model_name"] for model in available_models["models"]]
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# Create the dropdown
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model_dropdown = gr.Dropdown(choices=sorted(model_names), label="Select Model", value=model_names[0])
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download_button = gr.Button("Download Model/Missing Files")
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def download_model(model_name, force_download=False):
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# Find the selected model in the available models
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selected_model = next(model for model in available_models["models"] if model["model_name"] == model_name)
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# Get the base folder path and model-specific folder path
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base_folder_path = os.path.join(main_dir, "models", "f5tts")
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model_folder_path = os.path.join(base_folder_path, selected_model["folder_path"]) # Use specified folder path
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files_to_download = selected_model["files_to_download"]
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# Check if all files are already downloaded
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all_files_exists = all(
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os.path.exists(os.path.join(model_folder_path, file_name))
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for file_name in files_to_download
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)
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if all_files_exists and not force_download:
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return "All files are already downloaded. No need to download again."
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else:
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# Download the missing files
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for file_name, url in files_to_download.items():
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file_path = os.path.join(model_folder_path, file_name) # Save in the model-specific folder
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# Create the subdirectories for each file if they don't exist
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os.makedirs(os.path.dirname(file_path), exist_ok=True)
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if not os.path.exists(file_path) or force_download:
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print(f"Downloading {file_name} to {file_path}...")
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response = requests.get(url, stream=True)
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total_size_in_bytes = int(response.headers.get("content-length", 0))
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block_size = 1024 # 1 KiB
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progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
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with open(file_path, "wb") as file:
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for data in response.iter_content(block_size):
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progress_bar.update(len(data))
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file.write(data)
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progress_bar.close()
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return "Model downloaded successfully!"
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with gr.Row():
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download_status = gr.Textbox(label="Download Status")
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|
||||
download_button.click(download_model, inputs=model_dropdown, outputs=download_status)
|
||||
|
||||
def show_confirm_cancel(model_name):
|
||||
all_files_exists = all(os.path.exists(os.path.join(main_dir, "models", "f5tts", os.path.basename(file.split('?')[0]))) for model in available_models["models"] if model["model_name"] == model_name for file in model["files_to_download"])
|
||||
if all_files_exists:
|
||||
return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
|
||||
else:
|
||||
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
|
||||
|
||||
def confirm_download(model_name):
|
||||
download_status_text = download_model(model_name, force_download=True)
|
||||
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), download_status_text]
|
||||
|
||||
def cancel_download():
|
||||
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), ""]
|
||||
|
||||
with gr.Row():
|
||||
confirm_button = gr.Button("Download Anyway", visible=False)
|
||||
cancel_button = gr.Button("Cancel", visible=False)
|
||||
|
||||
download_button.click(show_confirm_cancel, inputs=model_dropdown, outputs=[confirm_button, download_button, cancel_button])
|
||||
confirm_button.click(confirm_download, inputs=model_dropdown, outputs=[confirm_button, download_button, cancel_button, download_status])
|
||||
cancel_button.click(cancel_download, inputs=None, outputs=[confirm_button, download_button, cancel_button, download_status])
|
||||
|
||||
|
||||
def get_voice_files():
|
||||
"""Get list of voice files with their status"""
|
||||
files = [
|
||||
f"🔴 {f}" if not Path(main_dir/'voices'/f).with_suffix('.reference.txt').exists() else f"🟢 {f}"
|
||||
for f in os.listdir(Path(main_dir/'voices'))
|
||||
if f.endswith('.wav')
|
||||
]
|
||||
return sorted(files)
|
||||
|
||||
with gr.Tab("Reference Text/Sample Manager"):
|
||||
with gr.Row():
|
||||
gr.Markdown("""
|
||||
### Reference Text/Sample Manager
|
||||
This tool helps you manage reference text files for your voice samples. Each WAV file should have a corresponding .reference.txt file
|
||||
containing the exact text that was spoken in the recording. This is important for voice cloning quality.<br>
|
||||
|
||||
Files marked in 🔴 red need a reference text file<br>
|
||||
Files marked in 🟢 green already have a reference text file<br>
|
||||
|
||||
It is important to note that F5-TTS only supports English and Chinese languages.
|
||||
|
||||
For more information on the F5-TTS engine, please visit the: [F5-TTS GitHub Repository](https://github.com/SWivid/F5-TTS/)
|
||||
""")
|
||||
|
||||
gr.Markdown("""
|
||||
#### Important Guidelines:
|
||||
* Reference WAV files should be no longer than 15 seconds in length, or it may affect playback speed.
|
||||
* You can play the audio below and listen to it. You then need to populate the Reference Text box with the exact spoken audio, preferably adding punctuation where appropriate:<br>
|
||||
- Period (.)<br>
|
||||
- Comma (,)<br>
|
||||
- Semi-colon (;)<br>
|
||||
- Apostrophe (') - Important for words like "I'm", "don't", "can't", etc.<br>
|
||||
* After saving the reference text file, you'll need to go to the main generation page and click "Refresh Server Settings" for the voice to be shown as available.
|
||||
""")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
# List all WAV files and their status
|
||||
file_list = gr.Dropdown(
|
||||
label="Voice Files",
|
||||
choices=get_voice_files(),
|
||||
value=None
|
||||
)
|
||||
|
||||
# Add audio player with small height
|
||||
audio_player = gr.Audio(
|
||||
label="Preview Voice Sample",
|
||||
type="filepath",
|
||||
interactive=False,
|
||||
elem_classes="small-audio-player" # Custom CSS class for sizing
|
||||
)
|
||||
|
||||
refresh_btn = gr.Button("Refresh List")
|
||||
|
||||
with gr.Column():
|
||||
current_text = gr.Textbox(
|
||||
label="Reference Text",
|
||||
placeholder="Enter the exact text that was spoken in the recording...",
|
||||
lines=5
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
save_btn = gr.Button("Save Reference Text", variant="primary")
|
||||
delete_btn = gr.Button("Delete Reference Text", variant="secondary")
|
||||
|
||||
status_text = gr.Textbox(label="Status", interactive=False)
|
||||
|
||||
# Add custom CSS to make the audio player smaller
|
||||
gr.HTML("""
|
||||
<style>
|
||||
.small-audio-player audio {
|
||||
height: 40px !important;
|
||||
margin-top: 0px !important;
|
||||
}
|
||||
.small-audio-player {
|
||||
margin-bottom: 10px !important;
|
||||
}
|
||||
</style>
|
||||
""")
|
||||
|
||||
def load_file_data(wav_file):
|
||||
if not wav_file:
|
||||
return None, "", "No file selected"
|
||||
|
||||
# Remove status emoji if present
|
||||
if wav_file.startswith("🔴 ") or wav_file.startswith("🟢 "):
|
||||
wav_file = wav_file[2:]
|
||||
|
||||
# Get paths
|
||||
wav_path = Path(main_dir)/'voices'/wav_file
|
||||
ref_path = wav_path.with_suffix('.reference.txt')
|
||||
|
||||
# Get reference text if it exists
|
||||
text = ""
|
||||
if ref_path.exists():
|
||||
with open(ref_path, 'r', encoding='utf-8') as f:
|
||||
text = f.read()
|
||||
status = "Loaded existing reference text"
|
||||
else:
|
||||
status = "No reference text file found"
|
||||
|
||||
return str(wav_path), text, status
|
||||
|
||||
def save_reference_text(wav_file, text):
|
||||
if not wav_file:
|
||||
return "No file selected"
|
||||
|
||||
# Remove status emoji if present
|
||||
if wav_file.startswith("🔴 ") or wav_file.startswith("🟢 "):
|
||||
wav_file = wav_file[2:]
|
||||
|
||||
ref_path = Path(main_dir)/'voices'/f"{Path(wav_file).stem}.reference.txt"
|
||||
|
||||
try:
|
||||
with open(ref_path, 'w', encoding='utf-8') as f:
|
||||
f.write(text)
|
||||
return "Reference text saved successfully!"
|
||||
except Exception as e:
|
||||
return f"Error saving reference text: {str(e)}"
|
||||
|
||||
def delete_reference_text(wav_file):
|
||||
if not wav_file:
|
||||
return "No file selected"
|
||||
|
||||
# Remove status emoji if present
|
||||
if wav_file.startswith("🔴 ") or wav_file.startswith("🟢 "):
|
||||
wav_file = wav_file[2:]
|
||||
|
||||
ref_path = Path(main_dir)/'voices'/f"{Path(wav_file).stem}.reference.txt"
|
||||
|
||||
try:
|
||||
if ref_path.exists():
|
||||
os.remove(ref_path)
|
||||
return "Reference text file deleted successfully!"
|
||||
return "No reference text file exists"
|
||||
except Exception as e:
|
||||
return f"Error deleting reference text: {str(e)}"
|
||||
|
||||
def refresh_file_list():
|
||||
return gr.Dropdown(choices=get_voice_files())
|
||||
|
||||
# Setup event handlers
|
||||
file_list.change(
|
||||
load_file_data,
|
||||
inputs=file_list,
|
||||
outputs=[audio_player, current_text, status_text]
|
||||
)
|
||||
save_btn.click(
|
||||
save_reference_text,
|
||||
inputs=[file_list, current_text],
|
||||
outputs=status_text
|
||||
).then(
|
||||
refresh_file_list,
|
||||
outputs=file_list
|
||||
)
|
||||
delete_btn.click(
|
||||
delete_reference_text,
|
||||
inputs=[file_list],
|
||||
outputs=status_text
|
||||
).then(
|
||||
refresh_file_list,
|
||||
outputs=file_list
|
||||
)
|
||||
refresh_btn.click(refresh_file_list, outputs=file_list)
|
||||
|
||||
###################################################################################################
|
||||
# REQUIRED CHANGE #
|
||||
# Add any engine specific help, bugs, issues, operating system specifc requirements/setup in here #
|
||||
# Please use Markdown format, so gr.Markdown() with your markdown inside it. #
|
||||
###################################################################################################
|
||||
with gr.Tab("Engine Help"):
|
||||
with gr.Row():
|
||||
gr.Markdown("""
|
||||
### 🟧 Where are the f5tts models stored?
|
||||
This extension will download the models to `/alltalk_tts/models/f5tts/` folder.
|
||||
|
||||
### 🟧 How do reference voices work?
|
||||
F5-TTS uses voice samples with corresponding reference text files for voice cloning:
|
||||
1. Place your WAV voice samples in the `/alltalk_tts/voices/` folder
|
||||
2. Create a matching `.reference.txt` file containing the exact text spoken in the recording
|
||||
3. Use the Reference Text Manager tab to create and manage these text files
|
||||
4. Only voice samples with valid reference text files will be available for use
|
||||
|
||||
For best results:
|
||||
- Keep voice samples under 15 seconds
|
||||
- Ensure the reference text exactly matches what is spoken
|
||||
- Use high-quality recordings with minimal background noise
|
||||
|
||||
### 🟧 Where are the outputs stored & Automatic output wav file deletion
|
||||
Voice outputs are stored in `/alltalk_tts/outputs/`. You can configure automatic maintenance deletion of old wav files by setting `Del WAV's older than` in the global settings.
|
||||
|
||||
> When `Disabled`, your output wav files will be left untouched.
|
||||
> When set to a setting `1 Day` or greater, your output wav files older than that time period will be automatically deleted on start-up of AllTalk.
|
||||
""")
|
||||
|
||||
|
||||
return app
|
||||
|
||||
################################
|
||||
# REQUIRED CHANGE #
|
||||
# Sets up the Gradio interface #
|
||||
################################
|
||||
# This function sets up the Gradio interface for your TTS engine's settings page.
|
||||
# You need to change the name of the function calls to match the names you set in the functions above.
|
||||
#
|
||||
# Specifically, you need to update the following:
|
||||
#
|
||||
# 1. The name of the function `xxxx_at_gradio_settings_page` to match your engine's name.
|
||||
# For example, if your engine is named "mytts", the function should be named `mytts_at_gradio_settings_page`.
|
||||
#
|
||||
# 2. The name of the function call `xxxx_model_alltalk_settings(model_config_data)`.
|
||||
# This should match the name you gave to the function that sets up the engine-specific settings page in Gradio.
|
||||
# If you named that function `mytts_model_alltalk_settings`, then the call should be:
|
||||
# `mytts_model_alltalk_settings(model_config_data)`
|
||||
#
|
||||
# After making these changes, this function will create and return the Gradio app for your TTS engine's settings page.
|
||||
def f5tts_at_gradio_settings_page(model_config_data):
|
||||
app = f5tts_model_alltalk_settings(model_config_data)
|
||||
return app
|
||||
def f5tts_at_gradio_settings_page(model_config_data):
|
||||
app = f5tts_model_alltalk_settings(model_config_data)
|
||||
return app
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,42 @@
|
||||
{
|
||||
"model_details": {
|
||||
"manufacturer_name": "F5-TTS",
|
||||
"manufacturer_website": "https://github.com/SWivid/F5-TTS",
|
||||
"model_description": "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
|
||||
},
|
||||
"model_capabilties": {
|
||||
"audio_format": "wav",
|
||||
"deepspeed_capable": false,
|
||||
"generationspeed_capable": true,
|
||||
"languages_capable": true,
|
||||
"lowvram_capable": true,
|
||||
"multimodel_capable": true,
|
||||
"repetitionpenalty_capable": false,
|
||||
"streaming_capable": false,
|
||||
"temperature_capable": false,
|
||||
"multivoice_capable": true,
|
||||
"pitch_capable": false,
|
||||
"windows_capable": true,
|
||||
"linux_capable": true,
|
||||
"mac_capable": true
|
||||
},
|
||||
"settings": {
|
||||
"def_character_voice": "female_01.wav",
|
||||
"def_narrator_voice": "female_01.wav",
|
||||
"deepspeed_enabled": false,
|
||||
"engine_installed": true,
|
||||
"generationspeed_set": 1,
|
||||
"lowvram_enabled": false,
|
||||
"pitch_set": 0,
|
||||
"repetitionpenalty_set": 10,
|
||||
"temperature_set": 0.75
|
||||
},
|
||||
"openai_voices": {
|
||||
"alloy": "female_01.wav",
|
||||
"echo": "female_01.wav",
|
||||
"fable": "female_01.wav",
|
||||
"nova": "female_01.wav",
|
||||
"onyx": "female_01.wav",
|
||||
"shimmer": "female_01.wavf"
|
||||
}
|
||||
}
|
||||
Reference in new issue
Block a user