update infer limits

model/utils_infer.py

@@ -38,12 +38,12 @@
 n_mel_channels = 100
 hop_length = 256
 target_rms = 0.1
-nfe_step = 32  # 16, 32
-cfg_strength = 2.0
-ode_method = "euler"
-sway_sampling_coef = -1.0
-speed = 1.0
-fix_duration = None
+#nfe_step = 32  # 16, 32
+#cfg_strength = 2.0
+#ode_method = "euler"
+#sway_sampling_coef = -1.0
+#speed = 1.0
+#fix_duration = None
 
 # -----------------------------------------
 
@@ -84,7 +84,7 @@ def chunk_text(text, max_chars=135):
 # load vocoder
 
 
-def load_vocoder(is_local=False, local_path=""):
+def load_vocoder(is_local=False, local_path="",device=device):
     if is_local:
         print(f"Load vocos from local path {local_path}")
         vocos = Vocos.from_hparams(f"{local_path}/config.yaml")
@@ -100,14 +100,14 @@ def load_vocoder(is_local=False, local_path=""):
 # load model for inference
 
 
-def load_model(model_cls, model_cfg, ckpt_path, vocab_file=""):
+def load_model(model_cls, model_cfg, ckpt_path, vocab_file="",ode_method="euler",use_ema=True,device=device):
     if vocab_file == "":
         vocab_file = "Emilia_ZH_EN"
         tokenizer = "pinyin"
     else:
         tokenizer = "custom"
 
-    print("\nvocab : ", vocab_file, tokenizer)
+    print("\nvocab : ", vocab_file)
     print("tokenizer : ", tokenizer)
     print("model : ", ckpt_path, "\n")
 
@@ -125,7 +125,7 @@ def load_model(model_cls, model_cfg, ckpt_path, vocab_file=""):
         vocab_char_map=vocab_char_map,
     ).to(device)
 
-    model = load_checkpoint(model, ckpt_path, device, use_ema=True)
+    model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema)
 
     return model
 
@@ -178,8 +178,7 @@ def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print):
 
 
 def infer_process(
-    ref_audio, ref_text, gen_text, model_obj, cross_fade_duration=0.15, speed=speed, show_info=print, progress=tqdm
-):
+    ref_audio, ref_text, gen_text, model_obj, cross_fade_duration=0.15, speed=1.0, show_info=print, progress=tqdm,nfe_step=32,cfg_strength=2,sway_sampling_coef=-1,fix_duration=None):
     # Split the input text into batches
     audio, sr = torchaudio.load(ref_audio)
     max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
@@ -188,14 +187,14 @@ def infer_process(
         print(f"gen_text {i}", gen_text)
 
     show_info(f"Generating audio in {len(gen_text_batches)} batches...")
-    return infer_batch_process((audio, sr), ref_text, gen_text_batches, model_obj, cross_fade_duration, speed, progress)
+    return infer_batch_process((audio, sr), ref_text, gen_text_batches, model_obj, cross_fade_duration, speed, progress,nfe_step,cfg_strength,sway_sampling_coef,fix_duration)
 
 
 # infer batches
 
 
 def infer_batch_process(
-    ref_audio, ref_text, gen_text_batches, model_obj, cross_fade_duration=0.15, speed=1, progress=tqdm
+        ref_audio, ref_text, gen_text_batches, model_obj, cross_fade_duration=0.15, speed=1, progress=tqdm,nfe_step=32,cfg_strength=2.0,sway_sampling_coef=-1,fix_duration=None
 ):
     audio, sr = ref_audio
     if audio.shape[0] > 1:
@@ -219,11 +218,14 @@ def infer_batch_process(
         text_list = [ref_text + gen_text]
         final_text_list = convert_char_to_pinyin(text_list)
 
-        # Calculate duration
-        ref_audio_len = audio.shape[-1] // hop_length
-        ref_text_len = len(ref_text.encode("utf-8"))
-        gen_text_len = len(gen_text.encode("utf-8"))
-        duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)
+        if fix_duration is not None:
+            duration = int(fix_duration * target_sample_rate / hop_length)
+        else:    
+            # Calculate duration
+            ref_audio_len = audio.shape[-1] // hop_length
+            ref_text_len = len(ref_text.encode("utf-8"))
+            gen_text_len = len(gen_text.encode("utf-8"))
+            duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)
 
         # inference
         with torch.inference_mode():
@@ -291,10 +293,7 @@ def infer_batch_process(
 
     return final_wave, target_sample_rate, combined_spectrogram
 
-
 # remove silence from generated wav
-
-
 def remove_silence_for_generated_wav(filename):
     aseg = AudioSegment.from_file(filename)
     non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)