Initial commit

zipvoice/luxvoice.py

@@ -0,0 +1,52 @@
+from zipvoice.modeling_utils import process_audio, generate, load_models_gpu, load_models_cpu
+from zipvoice.onnx_modeling import generate_cpu
+
+class LuxTTS:
+    """
+    LuxTTS class for encoding prompt and generating speech on cpu/cuda.
+    """
+
+    def __init__(self, model_path='YatharthS/LuxTTS', device='cuda', threads=4):
+        if model_path == 'YatharthS/LuxTTS':
+            model_path = None
+            
+        if device == 'cpu':
+            model, feature_extractor, vocos, tokenizer, transcriber = load_models_cpu(model_path, threads)
+            print("Loading model on CPU")
+        else:
+            model, feature_extractor, vocos, tokenizer, transcriber = load_models_gpu(model_path)
+            print("Loading model on GPU")
+
+        self.model = model
+        self.feature_extractor = feature_extractor
+        self.vocos = vocos
+        self.tokenizer = tokenizer
+        self.transcriber = transcriber
+        self.device = device
+        self.vocos.freq_range = 12000
+        
+
+
+    def encode_prompt(self, prompt_audio, duration=5, rms=0.001):
+        """encodes audio prompt according to duration and rms(volume control)"""
+        prompt_tokens, prompt_features_lens, prompt_features, prompt_rms = process_audio(prompt_audio, self.transcriber, self.tokenizer, self.feature_extractor, self.device, target_rms=rms, duration=duration)
+        encode_dict = {"prompt_tokens": prompt_tokens, 'prompt_features_lens': prompt_features_lens, 'prompt_features': prompt_features, 'prompt_rms': prompt_rms}
+
+        return encode_dict
+      
+    def generate_speech(self, text, encode_dict, num_steps=4, guidance_scale=3.0, t_shift=0.5, speed=1.0, return_smooth=False):
+        """encodes text and generates speech using flow matching model according to steps, guidance scale, and t_shift(like temp)"""
+      
+        prompt_tokens, prompt_features_lens, prompt_features, prompt_rms = encode_dict.values()
+
+        if return_smooth == True:
+            self.vocos.return_48k = False
+        else:
+            self.vocos.return_48k = True
+            
+        if self.device == 'cpu':
+            final_wav = generate_cpu(prompt_tokens, prompt_features_lens, prompt_features, prompt_rms, text, self.model, self.vocos, self.tokenizer, num_step=num_steps, guidance_scale=guidance_scale, t_shift=t_shift, speed=speed)
+        else:
+            final_wav = generate(prompt_tokens, prompt_features_lens, prompt_features, prompt_rms, text, self.model, self.vocos, self.tokenizer, num_step=num_steps, guidance_scale=guidance_scale, t_shift=t_shift, speed=speed)
+
+        return final_wav.cpu()