NeuralFalcon
commited on
Create scripts/api.py
Browse files- scripts/api.py +77 -0
scripts/api.py
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# It is helpful if you want to use it in a voice assistant project.
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# Know more about {your gradio app url}/?view=api. Example: http://127.0.0.1:7860/?view=api
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import shutil
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import os
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from gradio_client import Client
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# Ensure the output directory exists
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output_dir = "api_output"
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os.makedirs(output_dir, exist_ok=True)
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# Initialize the Gradio client
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api_url = "http://127.0.0.1:7860/"
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client = Client(api_url)
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def text_to_speech(
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text="Hello!!",
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model_name="kokoro-v0_19.pth",
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voice_name="af_bella",
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speed=1,
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pad_between_segments=0,
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remove_silence=False,
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minimum_silence=0.05,
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custom_voicepack=None,
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):
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"""
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Generates speech from text using a specified model and saves the audio file.
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Parameters:
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text (str): The text to convert to speech.
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model_name (str): The name of the model to use for synthesis.
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voice_name (str): The name of the voice to use.
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speed (float): The speed of speech.
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pad_between_segments (int): Padding between audio segments.
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remove_silence (bool): Whether to remove silence from the audio.
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minimum_silence (float): Minimum silence duration to consider.
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custom_voicepack (str): Path to the custom voice pack to use.
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Returns:
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str: Path to the saved audio file.
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"""
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# Call the API with provided parameters
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result = client.predict(
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text=text,
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model_name=model_name,
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voice_name=voice_name,
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speed=speed,
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pad_between_segments=pad_between_segments,
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remove_silence=remove_silence,
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minimum_silence=minimum_silence,
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custom_voicepack=custom_voicepack,
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api_name="/text_to_speech"
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)
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# Save the audio file in the specified directory
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save_at = f"{output_dir}/{os.path.basename(result)}"
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shutil.move(result, save_at)
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print(f"Saved at {save_at}")
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return save_at
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# Example usage
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if __name__ == "__main__":
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text="This is Kokoro TTS. I am a text-to-speech model and Super Fast."
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model_name="kokoro-v0_19.pth" #kokoro-v0_19-half.pth
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voice_name="af_bella" #get voice names
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speed=1
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add_silence_between_segments=0 #it use in large text
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remove_silence=False
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keep_silence_upto=0.05 #in seconds
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custom_voicepack=None
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audio_path = text_to_speech(text=text, model_name=model_name,
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voice_name=voice_name, speed=speed,
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pad_between_segments=add_silence_between_segments,
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remove_silence=remove_silence,
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minimum_silence=keep_silence_upto)
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print(f"Audio file saved at: {audio_path}")
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