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inference-cli.py

@@ -175,6 +175,32 @@ F5TTS_model_cfg = dict(
 )
 E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
 
+if model == "F5-TTS":
+
+    if ckpt_file == "": 
+       repo_name= "F5-TTS"
+       exp_name = "F5TTS_Base"
+       ckpt_step= 1200000
+       ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+
+    ema_model = load_model(DiT, F5TTS_model_cfg, ckpt_file,vocab_file)
+
+elif model == "E2-TTS":
+    if ckpt_file == "": 
+       repo_name= "E2-TTS"
+       exp_name = "E2TTS_Base"
+       ckpt_step= 1200000
+       ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+    
+    ema_model = load_model(UNetT, E2TTS_model_cfg, ckpt_file,vocab_file)
+
+asr_pipe = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-large-v3-turbo",
+    torch_dtype=torch.float16,
+    device=device,
+)
+
 def chunk_text(text, max_chars=135):
     """
     Splits the input text into chunks, each with a maximum number of characters.
@@ -206,26 +232,7 @@ def chunk_text(text, max_chars=135):
     #if not Path(ckpt_path).exists():
         #ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
 
-def infer_batch(ref_audio, ref_text, gen_text_batches, model,ckpt_file,file_vocab, remove_silence, cross_fade_duration=0.15):
-    if model == "F5-TTS":
-
-        if ckpt_file == "": 
-           repo_name= "F5-TTS"
-           exp_name = "F5TTS_Base"
-           ckpt_step= 1200000
-           ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-
-        ema_model = load_model(DiT, F5TTS_model_cfg, ckpt_file,file_vocab)
-
-    elif model == "E2-TTS":
-        if ckpt_file == "": 
-           repo_name= "E2-TTS"
-           exp_name = "E2TTS_Base"
-           ckpt_step= 1200000
-           ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-        
-        ema_model = load_model(UNetT, E2TTS_model_cfg, ckpt_file,file_vocab)
-
+def infer_batch(ref_audio, ref_text, gen_text_batches, model, remove_silence, cross_fade_duration=0.15):
     audio, sr = ref_audio
     if audio.shape[0] > 1:
         audio = torch.mean(audio, dim=0, keepdim=True)
@@ -342,13 +349,7 @@ def process_voice(ref_audio_orig, ref_text):
 
     if not ref_text.strip():
         print("No reference text provided, transcribing reference audio...")
-        pipe = pipeline(
-            "automatic-speech-recognition",
-            model="openai/whisper-large-v3-turbo",
-            torch_dtype=torch.float16,
-            device=device,
-        )
-        ref_text = pipe(
+        ref_text = asr_pipe(
             ref_audio,
             chunk_length_s=30,
             batch_size=128,
@@ -360,7 +361,7 @@ def process_voice(ref_audio_orig, ref_text):
         print("Using custom reference text...")
     return ref_audio, ref_text    
 
-def infer(ref_audio, ref_text, gen_text, model,ckpt_file,file_vocab, remove_silence, cross_fade_duration=0.15):
+def infer(ref_audio, ref_text, gen_text, model, remove_silence, cross_fade_duration=0.15):
     # Add the functionality to ensure it ends with ". "
     if not ref_text.endswith(". ") and not ref_text.endswith("。"):
         if ref_text.endswith("."):
@@ -376,10 +377,10 @@ def infer(ref_audio, ref_text, gen_text, model,ckpt_file,file_vocab, remove_sile
         print(f'gen_text {i}', gen_text)
     
     print(f"Generating audio using {model} in {len(gen_text_batches)} batches, loading models...")
-    return infer_batch((audio, sr), ref_text, gen_text_batches, model,ckpt_file,file_vocab, remove_silence, cross_fade_duration)
+    return infer_batch((audio, sr), ref_text, gen_text_batches, model, remove_silence, cross_fade_duration)
     
 
-def process(ref_audio, ref_text, text_gen, model,ckpt_file,file_vocab, remove_silence):
+def process(ref_audio, ref_text, text_gen, model, remove_silence):
     main_voice = {"ref_audio":ref_audio, "ref_text":ref_text}
     if "voices" not in config:
         voices = {"main": main_voice}
@@ -407,7 +408,7 @@ def process(ref_audio, ref_text, text_gen, model,ckpt_file,file_vocab, remove_si
         ref_audio = voices[voice]['ref_audio']
         ref_text = voices[voice]['ref_text']
         print(f"Voice: {voice}")
-        audio, spectragram = infer(ref_audio, ref_text, gen_text, model,ckpt_file,file_vocab, remove_silence)
+        audio, spectragram = infer(ref_audio, ref_text, gen_text, model,remove_silence)
         generated_audio_segments.append(audio)
 
     if generated_audio_segments:
@@ -426,4 +427,4 @@ def process(ref_audio, ref_text, text_gen, model,ckpt_file,file_vocab, remove_si
             print(f.name)
 
 
-process(ref_audio, ref_text, gen_text, model,ckpt_file,vocab_file, remove_silence)
+process(ref_audio, ref_text, gen_text, model, remove_silence)