lightmare/ai-voice-cloning
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Added option to skip transcribing if it exists in the output text file, because apparently whisperx will throw a "max files opened" error when using ROCm because it does not close some file descriptors if you're batch-transcribing or something, so poor little me, who's retranscribing his japanese dataset for the 305823042th time woke up to it partially done i am so mad I have to wait another few hours for it to continue when I was hoping to wake up to it done

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This commit is contained in:
mrq 2023-03-06 10:47:06 +00:00
parent 0e3bbc55f8
commit 14779a5020
2 changed files with 25 additions and 4 deletions
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22
src/utils.py
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@ -1037,7 +1037,7 @@ def whisper_transcribe( file, language=None ):
return result return result
def prepare_dataset( files, outdir, language=None, progress=None ): def prepare_dataset( files, outdir, language=None, skip_existings=False, progress=None ):
unload_tts() unload_tts()
global whisper_model global whisper_model
@ -1049,8 +1049,28 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
results = {} results = {}
transcription = [] transcription = []
previous_list = []
if skip_existings and os.path.exists(f'{outdir}/train.txt'):
parsed_list = []
with open(f'{outdir}/train.txt', 'r', encoding="utf-8") as f:
parsed_list = f.readlines()
for line in parsed_list:
match = re.findall(r"^(.+?)_\d+\.wav$", line.split("|")[0])
print(match)
if match is None or len(match) == 0:
continue
if match[0] not in previous_list:
previous_list.append(f'{match[0]}.wav')
for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress): for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
basename = os.path.basename(file) basename = os.path.basename(file)
if basename in previous_list:
print(f"Skipping already parsed file: {basename}")
continue
result = whisper_transcribe(file, language=language) result = whisper_transcribe(file, language=language)
results[basename] = result results[basename] = result
print(f"Transcribed file: {file}, {len(result['segments'])} found.") print(f"Transcribed file: {file}, {len(result['segments'])} found.")

7
src/webui.py
View File

@ -185,8 +185,8 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
gr.update(visible=j is not None), gr.update(visible=j is not None),
) )
def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ): def prepare_dataset_proxy( voice, language, skip_existings, progress=gr.Progress(track_tqdm=True) ):
return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, progress=progress ) return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, progress=progress )
def optimize_training_settings_proxy( *args, **kwargs ): def optimize_training_settings_proxy( *args, **kwargs ):
tup = optimize_training_settings(*args, **kwargs) tup = optimize_training_settings(*args, **kwargs)
@ -478,7 +478,8 @@ def setup_gradio():
with gr.Column(): with gr.Column():
dataset_settings = [ dataset_settings = [
gr.Dropdown( choices=voice_list, label="Dataset Source", type="value", value=voice_list[0] if len(voice_list) > 0 else "" ), gr.Dropdown( choices=voice_list, label="Dataset Source", type="value", value=voice_list[0] if len(voice_list) > 0 else "" ),
gr.Textbox(label="Language", value="en") gr.Textbox(label="Language", value="en"),
gr.Checkbox(label="Skip Already Transcribed", value=False)
] ]
prepare_dataset_button = gr.Button(value="Prepare") prepare_dataset_button = gr.Button(value="Prepare")
with gr.Column(): with gr.Column():