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