Add files via upload

2025-04-08 18:56:49 -07:00
parent 644ee0424c
commit f00ed0f0be
1 changed files with 106 additions and 0 deletions
@@ -0,0 +1,106 @@
+import os
+import io
+import numpy as np
+import torch
+import librosa
+import soundfile as sf
+from .tts_inferencer import TTSInferencer
+from .MegaTTS_utils import initialize
+from .AILab_MegaTTS import MegaTTS3
+
+class MegaTTS_VoiceMaker:
+    infer_instance_cache = None
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        if not getattr(MegaTTS3, 'initialization_done', False):
+            initialize()
+            MegaTTS3.initialization_done = True
+            
+        return {
+            "required": {
+                "audio_in": ("AUDIO", {"tooltip": "Input audio to be converted."}),
+                "voice_name": ("STRING", {"default": "my_voice", "tooltip": "Name of the voice to be used for conversion."}),
+                "path": ("STRING", {"default": "", "placeholder": "Voices", "tooltip": "Directory path where the voice files will be saved. If empty, will use default 'Voices' folder."}),
+                "trim_silence": ("BOOLEAN", {"default": True, "tooltip": "Whether to trim silence from the audio."}),
+                "normalize_volume": ("BOOLEAN", {"default": True, "tooltip": "Whether to normalize the volume of the audio."}),
+                "max_duration": ("FLOAT", {"default": 10.0, "min": 1.0, "max": 60.0, "step": 0.5, "tooltip": "Maximum duration of the audio in seconds."})
+            }
+        }
+
+    RETURN_TYPES = ("AUDIO", "STRING",)
+    RETURN_NAMES = ("audio_out", "voice_path",)
+    FUNCTION = "convert_voice"
+    CATEGORY = "🧪AILab/🔊Audio"
+
+    def convert_voice(self, audio_in, voice_name, path="", trim_silence=True, normalize_volume=True, max_duration=10.0):
+        if MegaTTS_VoiceMaker.infer_instance_cache is not None:
+            infer_instance = MegaTTS_VoiceMaker.infer_instance_cache
+        else:
+            infer_instance = MegaTTS_VoiceMaker.infer_instance_cache = TTSInferencer()
+        
+        if audio_in is None or not isinstance(audio_in, dict) or 'waveform' not in audio_in:
+            return ({"waveform": torch.zeros(1, 1), "sample_rate": 24000}, "No input audio provided")
+        
+        waveform = audio_in['waveform']
+        sample_rate = audio_in.get('sample_rate', 44100)
+        
+        if not torch.is_tensor(waveform):
+            return (audio_in, "Error: Waveform must be a tensor")
+        
+        samples = waveform.cpu().numpy()
+        if len(samples.shape) > 1:
+            samples = samples.squeeze()
+        samples = samples.astype(np.float32)
+        
+        if sample_rate != infer_instance.sr:
+            samples = librosa.resample(samples, orig_sr=sample_rate, target_sr=infer_instance.sr)
+        
+        max_samples = int(max_duration * infer_instance.sr)
+        if len(samples) > max_samples:
+            samples = samples[:max_samples]
+        
+        if trim_silence:
+            samples, _ = librosa.effects.trim(samples, top_db=30)
+        
+        if normalize_volume:
+            samples = librosa.util.normalize(samples)
+        
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        voices_dir = os.path.join(current_dir, "Voices" if path == "" else path)
+        os.makedirs(voices_dir, exist_ok=True)
+        
+        output_wav_path = os.path.join(voices_dir, f"{voice_name}.wav")
+        output_npy_path = os.path.join(voices_dir, f"{voice_name}.npy")
+        
+        sf.write(output_wav_path, samples, infer_instance.sr)
+        
+        wav_io = io.BytesIO()
+        sf.write(wav_io, samples, infer_instance.sr, format='WAV')
+        voice_data = wav_io.getvalue()
+        
+        resource_context = infer_instance.preprocess(
+            voice_data, 
+            use_encoder_mode=True,
+            topk_dur=1
+        )
+        
+        vae_latent = resource_context['vae_latent'].cpu().numpy()
+        np.save(output_npy_path, vae_latent)
+        
+        status = f"✅ Successfully processed and saved reference voice '{voice_name}'\n"
+        status += f"• WAV: {output_wav_path}\n"
+        status += f"• NPY: {output_npy_path}\n"
+        status += f"• Duration: {len(samples)/infer_instance.sr:.2f} seconds\n"
+        status += f"• Sample rate: {infer_instance.sr}Hz\n"
+        status += f"• Feature shape: {vae_latent.shape}"
+        
+        return (audio_in, status)
+
+NODE_CLASS_MAPPINGS = {
+    "MegaTTS_VoiceMaker": MegaTTS_VoiceMaker
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MegaTTS_VoiceMaker": "Voice Maker for MegaTTS3"
+}