update

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="",device=device):
+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")
@@ -100,7 +100,7 @@ def load_vocoder(is_local=False, local_path="",device=device):
 # load model for inference
 
 
-def load_model(model_cls, model_cfg, ckpt_path, vocab_file="",ode_method="euler",use_ema=True,device=device):
+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"
@@ -178,7 +178,19 @@ 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=1.0, show_info=print, progress=tqdm,nfe_step=32,cfg_strength=2,sway_sampling_coef=-1,fix_duration=None):
+    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))
@@ -187,14 +199,36 @@ 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,nfe_step,cfg_strength,sway_sampling_coef,fix_duration)
+    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,nfe_step=32,cfg_strength=2.0,sway_sampling_coef=-1,fix_duration=None
+    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:
@@ -220,7 +254,7 @@ def infer_batch_process(
 
         if fix_duration is not None:
             duration = int(fix_duration * target_sample_rate / hop_length)
-        else:    
+        else:
             # Calculate duration
             ref_audio_len = audio.shape[-1] // hop_length
             ref_text_len = len(ref_text.encode("utf-8"))
@@ -293,6 +327,7 @@ 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)