forked from mrq/ai-voice-cloning
rely on the whisper.json for handling a lot more things
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382a3e4104
194
src/utils.py
194
src/utils.py
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@ -33,7 +33,7 @@ from datetime import datetime
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from datetime import timedelta
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from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
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from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
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from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir, get_voices
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from tortoise.utils.text import split_and_recombine_text
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from tortoise.utils.device import get_device_name, set_device_name, get_device_count, get_device_vram
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@ -1059,6 +1059,47 @@ def validate_waveform( waveform, sample_rate ):
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return False
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return True
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def transcribe_dataset( voice, language=None, skip_existings=False, progress=None ):
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unload_tts()
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global whisper_model
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if whisper_model is None:
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load_whisper_model(language=language)
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results = {}
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files = sorted( get_voices(load_latents=False)[voice] )
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indir = f'./training/{voice}/'
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infile = f'{indir}/whisper.json'
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os.makedirs(f'{indir}/audio/', exist_ok=True)
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if os.path.exists(infile):
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results = json.load(open(infile, 'r', encoding="utf-8"))
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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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if basename in results and skip_existings:
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print(f"Skipping already parsed file: {basename}")
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continue
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results[basename] = whisper_transcribe(file, language=language)
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# lazy copy
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waveform, sampling_rate = torchaudio.load(file)
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torchaudio.save(f"{indir}/audio/{basename}", waveform, sampling_rate)
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with open(infile, 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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do_gc()
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unload_whisper()
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return f"Processed dataset to: {indir}"
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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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@ -1066,148 +1107,71 @@ def slice_dataset( voice, start_offset=0, end_offset=0 ):
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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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results = json.load(open(infile, 'r', encoding="utf-8"))
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transcription = []
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files = 0
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segments = 0
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for filename in results:
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idx = 0
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files += 1
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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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segments +=1
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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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sliced = waveform[:, start:end]
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file = filename.replace(".wav", f"_{pad(segment['id'], 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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if not validate_waveform( sliced, sampling_rate ):
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print(f"Invalid waveform segment ({segment['start']}:{segment['end']}): {file}, 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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torchaudio.save(f"{indir}/audio/{file}", sliced, 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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return f"Sliced segments: {files} => {segments}."
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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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if whisper_model is None:
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load_whisper_model(language=language)
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os.makedirs(f'{outdir}/audio/', exist_ok=True)
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results = {}
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transcription = []
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files = sorted(files)
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previous_list = []
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if skip_existings and os.path.exists(f'{outdir}/train.txt'):
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parsed_list = []
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with open(f'{outdir}/train.txt', 'r', encoding="utf-8") as f:
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parsed_list = f.readlines()
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for line in parsed_list:
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match = re.findall(r"^(.+?)_\d+\.wav$", line.split("|")[0])
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if match is None or len(match) == 0:
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continue
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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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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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if basename in previous_list:
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print(f"Skipping already parsed file: {basename}")
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continue
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result = whisper_transcribe(file, language=language)
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results[basename] = result
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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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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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do_gc()
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with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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unload_whisper()
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joined = "\n".join(transcription)
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if not skip_existings:
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with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
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f.write(joined)
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return f"Processed dataset to: {outdir}\n{joined}"
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def prepare_validation_dataset( voice, text_length, audio_length ):
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def prepare_dataset( voice, use_segments, text_length, audio_length ):
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indir = f'./training/{voice}/'
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infile = f'{indir}/dataset.txt'
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if not os.path.exists(infile):
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infile = f'{indir}/train.txt'
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with open(f'{indir}/train.txt', 'r', encoding="utf-8") as src:
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with open(f'{indir}/dataset.txt', 'w', encoding="utf-8") as dst:
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dst.write(src.read())
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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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lines = f.readlines()
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results = json.load(open(infile, 'r', encoding="utf-8"))
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validation = []
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training = []
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lines = {
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'training': [],
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'validation': [],
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}
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for line in lines:
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split = line.split("|")
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filename = split[0]
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text = split[1]
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culled = len(text) < text_length
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for filename in results:
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result = results[filename]
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segments = result['segments'] if use_segments else [{'text': result['text']}]
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for segment in segments:
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text = segment['text'].strip()
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file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav") if use_segments else filename
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if not culled and audio_length > 0:
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metadata = torchaudio.info(f'{indir}/{filename}')
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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culled = duration < audio_length
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culled = len(text) < text_length
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if not culled and audio_length > 0:
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metadata = torchaudio.info(f'{indir}/audio/{file}')
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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culled = duration < audio_length
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if culled:
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validation.append(line.strip())
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else:
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training.append(line.strip())
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lines['training' if not culled else 'validation'].append(f'audio/{file}|{text}')
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training_joined = "\n".join(lines['training'])
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validation_joined = "\n".join(lines['validation'])
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with open(f'{indir}/train.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(training))
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f.write(training_joined)
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with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(validation))
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f.write(validation_joined)
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msg = f"Culled {len(validation)}/{len(lines)} lines."
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print(msg)
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msg = f"Prepared {len(lines['training'])} lines (validation: {len(lines['validation'])}).\n{training_joined}\n\n{validation_joined}"
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return msg
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def calc_iterations( epochs, lines, batch_size ):
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26
src/webui.py
26
src/webui.py
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@ -182,16 +182,19 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
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gr.update(visible=j is not None),
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)
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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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def prepare_dataset_proxy( voice, language, validation_text_length, validation_audio_length, skip_existings, slice_audio, progress=gr.Progress(track_tqdm=False) ):
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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, progress=progress )
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message = transcribe_dataset( voice=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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message = prepare_dataset( voice, use_segments=slice_audio, text_length=validation_text_length, audio_length=validation_audio_length )
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messages.append(message)
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return "\n".join(messages)
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def update_args_proxy( *args ):
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@ -421,8 +424,8 @@ 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="(Re)Create Validation Dataset")
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slice_dataset_button = gr.Button(value="(Re)Slice Audio")
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prepare_dataset_button = gr.Button(value="(Re)Create Dataset")
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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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@ -654,7 +657,7 @@ def setup_gradio():
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inputs=None,
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outputs=[
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GENERATE_SETTINGS['voice'],
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dataset_settings[0],
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DATASET_SETTINGS['voice'],
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history_voices
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]
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)
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@ -742,10 +745,11 @@ def setup_gradio():
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inputs=dataset_settings,
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outputs=prepare_dataset_output #console_output
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)
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prepare_validation_button.click(
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prepare_validation_dataset,
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prepare_dataset_button.click(
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prepare_dataset,
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inputs=[
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dataset_settings[0],
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DATASET_SETTINGS['voice'],
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DATASET_SETTINGS['slice'],
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DATASET_SETTINGS['validation_text_length'],
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DATASET_SETTINGS['validation_audio_length'],
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],
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@ -754,7 +758,7 @@ def setup_gradio():
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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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DATASET_SETTINGS['voice']
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],
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outputs=prepare_dataset_output
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)
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