forked from camenduru/ai-voice-cloning
split slicing dataset routine so it can be done after the fact
This commit is contained in:
parent
e3fdb79b49
commit
94551fb9ac
96
src/utils.py
96
src/utils.py
|
|
@ -1051,7 +1051,56 @@ def whisper_transcribe( file, language=None ):
|
|||
result['segments'].append(reparsed)
|
||||
return result
|
||||
|
||||
def prepare_dataset( files, outdir, language=None, skip_existings=False, slice_audio=False, progress=None ):
|
||||
def validate_waveform( waveform, sample_rate ):
|
||||
if not torch.any(waveform < 0):
|
||||
return False
|
||||
|
||||
if waveform.shape[-1] < (.6 * sample_rate):
|
||||
return False
|
||||
return True
|
||||
|
||||
def slice_dataset( voice, start_offset=0, end_offset=0 ):
|
||||
indir = f'./training/{voice}/'
|
||||
infile = f'{indir}/whisper.json'
|
||||
|
||||
if not os.path.exists(infile):
|
||||
raise Exception(f"Missing dataset: {infile}")
|
||||
|
||||
with open(infile, 'r', encoding="utf-8") as f:
|
||||
results = json.load(f)
|
||||
|
||||
transcription = []
|
||||
for filename in results:
|
||||
idx = 0
|
||||
result = results[filename]
|
||||
waveform, sampling_rate = torchaudio.load(f'./voices/{voice}/{filename}')
|
||||
|
||||
for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
|
||||
start = int((segment['start'] + start_offset) * sampling_rate)
|
||||
end = int((segment['end'] + end_offset) * sampling_rate)
|
||||
|
||||
sliced_waveform = waveform[:, start:end]
|
||||
sliced_name = filename.replace(".wav", f"_{pad(idx, 4)}.wav")
|
||||
|
||||
if not validate_waveform( sliced_waveform, sampling_rate ):
|
||||
print(f"Invalid waveform segment ({segment['start']}:{segment['end']}): {sliced_name}, skipping...")
|
||||
continue
|
||||
|
||||
torchaudio.save(f"{indir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
|
||||
|
||||
idx = idx + 1
|
||||
line = f"audio/{sliced_name}|{segment['text'].strip()}"
|
||||
transcription.append(line)
|
||||
with open(f'{indir}/train.txt', 'a', encoding="utf-8") as f:
|
||||
f.write(f'\n{line}')
|
||||
|
||||
joined = "\n".join(transcription)
|
||||
with open(f'{indir}/train.txt', 'w', encoding="utf-8") as f:
|
||||
f.write(joined)
|
||||
|
||||
return f"Processed dataset to: {indir}\n{joined}"
|
||||
|
||||
def prepare_dataset( files, outdir, language=None, skip_existings=False, progress=None ):
|
||||
unload_tts()
|
||||
|
||||
global whisper_model
|
||||
|
|
@ -1079,13 +1128,6 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, slice_a
|
|||
if match[0] not in previous_list:
|
||||
previous_list.append(f'{match[0].split("/")[-1]}.wav')
|
||||
|
||||
def validate_waveform( waveform, sample_rate ):
|
||||
if not torch.any(waveform < 0):
|
||||
return False
|
||||
|
||||
if waveform.shape[-1] < (.6 * sampling_rate):
|
||||
return False
|
||||
return True
|
||||
|
||||
for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
|
||||
basename = os.path.basename(file)
|
||||
|
|
@ -1099,38 +1141,16 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, slice_a
|
|||
print(f"Transcribed file: {file}, {len(result['segments'])} found.")
|
||||
|
||||
waveform, sampling_rate = torchaudio.load(file)
|
||||
num_channels, num_frames = waveform.shape
|
||||
|
||||
if not slice_audio:
|
||||
if not validate_waveform( waveform, sampling_rate ):
|
||||
print(f"Invalid waveform: {basename}, skipping...")
|
||||
continue
|
||||
if not validate_waveform( waveform, sampling_rate ):
|
||||
print(f"Invalid waveform: {basename}, skipping...")
|
||||
continue
|
||||
|
||||
torchaudio.save(f"{outdir}/audio/{basename}", waveform, sampling_rate)
|
||||
line = f"audio/{basename}|{result['text'].strip()}"
|
||||
transcription.append(line)
|
||||
with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
|
||||
f.write(f'\n{line}')
|
||||
else:
|
||||
idx = 0
|
||||
for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
|
||||
start = int(segment['start'] * sampling_rate)
|
||||
end = int(segment['end'] * sampling_rate)
|
||||
|
||||
sliced_waveform = waveform[:, start:end]
|
||||
sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
|
||||
|
||||
if not validate_waveform( sliced_waveform, sampling_rate ):
|
||||
print(f"Invalid waveform segment ({segment['start']}:{segment['end']}): {sliced_name}, skipping...")
|
||||
continue
|
||||
|
||||
torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
|
||||
|
||||
idx = idx + 1
|
||||
line = f"audio/{sliced_name}|{segment['text'].strip()}"
|
||||
transcription.append(line)
|
||||
with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
|
||||
f.write(f'\n{line}')
|
||||
torchaudio.save(f"{outdir}/audio/{basename}", waveform, sampling_rate)
|
||||
line = f"audio/{basename}|{result['text'].strip()}"
|
||||
transcription.append(line)
|
||||
with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
|
||||
f.write(f'\n{line}')
|
||||
|
||||
do_gc()
|
||||
|
||||
|
|
|
|||
15
src/webui.py
15
src/webui.py
|
|
@ -184,8 +184,11 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
|
|||
|
||||
def prepare_dataset_proxy( voice, language, validation_text_length, validation_audio_length, skip_existings, slice_audio, progress=gr.Progress(track_tqdm=True) ):
|
||||
messages = []
|
||||
message = prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, slice_audio=slice_audio, progress=progress )
|
||||
message = prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, progress=progress )
|
||||
messages.append(message)
|
||||
if slice_audio:
|
||||
message = slice_dataset( voice )
|
||||
messages.append(message)
|
||||
if validation_text_length > 0 or validation_audio_length > 0:
|
||||
message = prepare_validation_dataset( voice, text_length=validation_text_length, audio_length=validation_audio_length )
|
||||
messages.append(message)
|
||||
|
|
@ -418,7 +421,8 @@ def setup_gradio():
|
|||
|
||||
with gr.Row():
|
||||
transcribe_button = gr.Button(value="Transcribe")
|
||||
prepare_validation_button = gr.Button(value="Prepare Validation")
|
||||
prepare_validation_button = gr.Button(value="(Re)Create Validation Dataset")
|
||||
slice_dataset_button = gr.Button(value="(Re)Slice Audio")
|
||||
|
||||
with gr.Row():
|
||||
EXEC_SETTINGS['whisper_backend'] = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
|
||||
|
|
@ -747,6 +751,13 @@ def setup_gradio():
|
|||
],
|
||||
outputs=prepare_dataset_output #console_output
|
||||
)
|
||||
slice_dataset_button.click(
|
||||
slice_dataset,
|
||||
inputs=[
|
||||
dataset_settings[0]
|
||||
],
|
||||
outputs=prepare_dataset_output
|
||||
)
|
||||
|
||||
training_refresh_dataset.click(
|
||||
lambda: gr.update(choices=get_dataset_list()),
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user