import transcribe from utils_infer

src/f5_tts/train/finetune_gradio.py

@@ -26,12 +26,13 @@ from datasets import Dataset as Dataset_
 from datasets.arrow_writer import ArrowWriter
 from safetensors.torch import save_file
 from scipy.io import wavfile
-from transformers import pipeline
 from cached_path import cached_path
 from f5_tts.api import F5TTS
 from f5_tts.model.utils import convert_char_to_pinyin
+from f5_tts.infer.utils_infer import transcribe
 from importlib.resources import files
 
+
 training_process = None
 system = platform.system()
 python_executable = sys.executable or "python"
@@ -47,8 +48,6 @@ file_train = "src/f5_tts/train/finetune_cli.py"
 
 device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 
-pipe = None
-
 
 # Save settings from a JSON file
 def save_settings(
@@ -390,17 +389,15 @@ def start_training(
     logger="wandb",
     ch_8bit_adam=False,
 ):
-    global training_process, tts_api, stop_signal, pipe
+    global training_process, tts_api, stop_signal
 
-    if tts_api is not None or pipe is not None:
+    if tts_api is not None:
         if tts_api is not None:
             del tts_api
-        if pipe is not None:
-            del pipe
+
         gc.collect()
         torch.cuda.empty_cache()
         tts_api = None
-        pipe = None
 
     path_project = os.path.join(path_data, dataset_name)
 
@@ -652,27 +649,6 @@ def create_data_project(name, tokenizer_type):
     return gr.update(choices=project_list, value=name)
 
 
-def transcribe(file_audio, language="english"):
-    global pipe
-
-    if pipe is None:
-        pipe = pipeline(
-            "automatic-speech-recognition",
-            model="openai/whisper-large-v3-turbo",
-            torch_dtype=torch.float16,
-            device=device,
-        )
-
-    text_transcribe = pipe(
-        file_audio,
-        chunk_length_s=30,
-        batch_size=128,
-        generate_kwargs={"task": "transcribe", "language": language},
-        return_timestamps=False,
-    )["text"].strip()
-    return text_transcribe
-
-
 def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
     path_project = os.path.join(path_data, name_project)
     path_dataset = os.path.join(path_project, "dataset")