Delete AILab_MegaTTS.py
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AI Lab
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import os
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import torch
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import folder_paths
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from .tts_inferencer import TTSInferencer
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from .MegaTTS_utils import get_voice_samples, get_voice_path, check_and_download_models, initialize
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models_dir = folder_paths.models_dir
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model_path = os.path.join(models_dir, "TTS")
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class MegaTTS3:
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infer_instance_cache = None
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initialization_done = False
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@classmethod
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def INPUT_TYPES(s):
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voice_samples = get_voice_samples()
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default_voice = voice_samples[0] if voice_samples else ""
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return {
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"required": {
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"input_text": ("STRING", {"multiline": True, "default": "", "placeholder": "Enter the text you want to convert to speech", "tooltip": "Enter the text you want to convert to speech"}),
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"language": (["en", "zh"], {"default": "en", "tooltip": "Select the language of your input text"}),
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"generation_quality": ("INT", {"default": 32, "min": 1, "step": 1, "tooltip": "Higher number = better quality but slower generation"}),
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"pronunciation_strength": ("FLOAT", {"default": 1.4, "min": 0.1, "step": 0.1, "tooltip": "How closely to follow the text pronunciation"}),
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"voice_similarity": ("FLOAT", {"default": 3, "min": 0.1, "step": 0.1, "tooltip": "How similar to the reference voice"}),
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"reference_voice": (voice_samples, {"default": default_voice, "tooltip": "Select a reference voice"})
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}
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("generated_audio",)
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FUNCTION = "generate_speech"
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CATEGORY = "🧪AILab/🔊Audio"
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def generate_speech(self, input_text, language, generation_quality,
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pronunciation_strength, voice_similarity,
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reference_voice):
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if not MegaTTS3.initialization_done:
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initialize()
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MegaTTS3.initialization_done = True
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if MegaTTS3.infer_instance_cache is not None:
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infer_instance = MegaTTS3.infer_instance_cache
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else:
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infer_instance = MegaTTS3.infer_instance_cache = TTSInferencer()
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if reference_voice is None:
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raise Exception("Reference voice must be provided")
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voice_path = get_voice_path(reference_voice)
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latent_path = voice_path.replace('.wav', '.npy')
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if not os.path.exists(latent_path):
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raise Exception("Voice feature file not found. Please ensure .npy file exists for selected voice.")
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with open(voice_path, 'rb') as file:
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voice_data = file.read()
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print(f"Using reference voice: {reference_voice}")
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resource_context = infer_instance.preprocess(
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voice_data,
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latent_file=latent_path
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)
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audio_output = infer_instance.forward(
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resource_context,
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input_text,
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language_type=language,
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time_step=generation_quality,
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p_w=pronunciation_strength,
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t_w=voice_similarity
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)
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import gc
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infer_ins = MegaTTS3.infer_instance_cache
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infer_ins.clean()
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MegaTTS3.infer_instance_cache = None
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gc.collect()
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torch.cuda.empty_cache()
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return (audio_output,)
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class MegaTTS3S:
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infer_instance_cache = None
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@classmethod
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def INPUT_TYPES(s):
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voice_samples = get_voice_samples()
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default_voice = voice_samples[0] if voice_samples else ""
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return {
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"required": {
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"input_text": ("STRING", {"multiline": True, "default": "", "placeholder": "Enter the text you want to convert to speech", "tooltip": "Enter the text you want to convert to speech"}),
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"language": (["en", "zh"], {"default": "en", "tooltip": "Select the language of your input text"}),
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"reference_voice": (voice_samples, {"default": default_voice, "tooltip": "Select a reference voice"})
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}
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("generated_audio",)
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FUNCTION = "generate_speech"
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CATEGORY = "🧪AILab/🔊Audio"
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def generate_speech(self, input_text, language, reference_voice):
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if not MegaTTS3.initialization_done:
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initialize()
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MegaTTS3.initialization_done = True
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if MegaTTS3S.infer_instance_cache is not None:
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infer_instance = MegaTTS3S.infer_instance_cache
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else:
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infer_instance = MegaTTS3S.infer_instance_cache = TTSInferencer()
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if reference_voice is None:
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raise Exception("Reference voice must be provided")
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voice_path = get_voice_path(reference_voice)
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latent_path = voice_path.replace('.wav', '.npy')
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if not os.path.exists(latent_path):
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raise Exception("Voice feature file not found. Please ensure .npy file exists for selected voice.")
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with open(voice_path, 'rb') as file:
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voice_data = file.read()
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print(f"Using reference voice: {reference_voice}")
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resource_context = infer_instance.preprocess(
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voice_data,
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latent_file=latent_path
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)
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audio_output = infer_instance.forward(
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resource_context,
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input_text,
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language_type=language,
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time_step=32,
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p_w=1.6,
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t_w=2.5
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)
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import gc
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infer_ins = MegaTTS3S.infer_instance_cache
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infer_ins.clean()
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MegaTTS3S.infer_instance_cache = None
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gc.collect()
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torch.cuda.empty_cache()
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return (audio_output,)
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NODE_CLASS_MAPPINGS = {
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"MegaTTS3": MegaTTS3,
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"MegaTTS3S": MegaTTS3S
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"MegaTTS3": "MegaTTS3",
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"MegaTTS3S": "MegaTTS3 (Simple)"
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}
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