forked from mrq/ai-voice-cloning
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9
models/.template.valle.yaml
Executable file
9
models/.template.valle.yaml
Executable file
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@ -0,0 +1,9 @@
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data_dirs: [./training/${voice}/valle/]
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spkr_name_getter: "lambda p: p.parts[-3]"
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model: ${model_name}
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batch_size: ${batch_size}
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eval_batch_size: ${validation_batch_size}
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eval_every: ${validation_rate}
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sampling_temperature: 1.0
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163
src/utils.py
163
src/utils.py
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@ -20,6 +20,8 @@ import subprocess
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import psutil
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import yaml
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import hashlib
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import io
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import gzip
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import tqdm
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import torch
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@ -45,6 +47,7 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
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TTSES = ['tortoise'] # + ['vall-e']
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GENERATE_SETTINGS_ARGS = None
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@ -56,6 +59,16 @@ RESAMPLERS = {}
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MIN_TRAINING_DURATION = 0.6
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MAX_TRAINING_DURATION = 11.6097505669
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VALLE_ENABLED = False
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try:
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from vall_e.emb.qnt import encode as quantize
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from vall_e.emb.g2p import encode as phonemize
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VALLE_ENABLED = True
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except Exception as e:
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pass
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args = None
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tts = None
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tts_loading = False
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@ -1195,7 +1208,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
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messages.append(f"Sliced segments: {files} => {segments}.")
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return "\n".join(messages)
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def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=False ):
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def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=True ):
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indir = f'./training/{voice}/'
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infile = f'{indir}/whisper.json'
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messages = []
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@ -1273,6 +1286,8 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=F
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continue
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waveform, sample_rate = torchaudio.load(path)
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num_channels, num_frames = waveform.shape
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duration = num_frames / sample_rate
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error = validate_waveform( waveform, sample_rate )
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if error:
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@ -1281,21 +1296,43 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=F
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messages.append(message)
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errored += 1
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continue
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culled = len(text) < text_length
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if not culled and audio_length > 0:
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num_channels, num_frames = waveform.shape
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duration = num_frames / sample_rate
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culled = duration < audio_length
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# for when i add in a little treat ;), as it requires normalized text
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if normalize and length(normalized_text) < 200:
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if normalize and len(normalized_text) < 200:
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line = f'audio/{file}|{text}|{normalized_text}'
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else:
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line = f'audio/{file}|{text}'
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lines['training' if not culled else 'validation'].append(line)
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if culled or not VALLE_ENABLED:
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continue
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# VALL-E dataset
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os.makedirs(f'{indir}/valle/', exist_ok=True)
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try:
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from vall_e.emb.qnt import encode as quantize
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from vall_e.emb.g2p import encode as phonemize
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if waveform.shape[0] == 2:
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waveform = wav[:1]
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quantized = quantize( waveform, sample_rate ).cpu()
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torch.save(quantized, f'{indir}/valle/{file.replace(".wav",".qnt.pt")}')
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phonemes = phonemize(normalized_text)
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open(f'{indir}/valle/{file.replace(".wav",".phn.txt")}', 'w', encoding='utf-8').write(" ".join(phonemes))
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except Exception as e:
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print(e)
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pass
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training_joined = "\n".join(lines['training'])
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validation_joined = "\n".join(lines['validation'])
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@ -1538,21 +1575,27 @@ def save_training_settings( **kwargs ):
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settings['source_model'] = f"pretrain_model_gpt: '{settings['source_model']}'"
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settings['resume_state'] = f"# resume_state: '{settings['resume_state']}'"
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with open(f'./models/.template.yaml', 'r', encoding="utf-8") as f:
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yaml = f.read()
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def use_template(template, out):
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with open(template, 'r', encoding="utf-8") as f:
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yaml = f.read()
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# i could just load and edit the YAML directly, but this is easier, as I don't need to bother with path traversals
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for k in settings:
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if settings[k] is None:
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continue
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yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
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# i could just load and edit the YAML directly, but this is easier, as I don't need to bother with path traversals
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for k in settings:
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if settings[k] is None:
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continue
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yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
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outyaml = f'./training/{settings["voice"]}/train.yaml'
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with open(outyaml, 'w', encoding="utf-8") as f:
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f.write(yaml)
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with open(out, 'w', encoding="utf-8") as f:
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f.write(yaml)
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use_template(f'./models/.template.dlas.yaml', f'./training/{settings["voice"]}/train.yaml')
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messages.append(f"Saved training output to: {outyaml}")
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settings['model_name'] = "ar"
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use_template(f'./models/.template.valle.yaml', f'./training/{settings["voice"]}/ar.yaml')
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settings['model_name'] = "nar"
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use_template(f'./models/.template.valle.yaml', f'./training/{settings["voice"]}/nar.yaml')
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messages.append(f"Saved training output")
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return settings, messages
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def import_voices(files, saveAs=None, progress=None):
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@ -1743,17 +1786,22 @@ def setup_args():
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'latents-lean-and-mean': True,
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'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
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'voice-fixer-use-cuda': True,
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'force-cpu-for-conditioning-latents': False,
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'defer-tts-load': False,
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'device-override': None,
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'prune-nonfinal-outputs': True,
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'vocoder-model': VOCODERS[-1],
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'concurrency-count': 2,
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'autocalculate-voice-chunk-duration-size': 0,
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'autocalculate-voice-chunk-duration-size': 10,
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'output-sample-rate': 44100,
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'output-volume': 1,
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'tts-backend': TTSES[0],
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'autoregressive-model': None,
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'vocoder-model': VOCODERS[-1],
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'whisper-backend': 'openai/whisper',
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'whisper-model': "base",
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@ -1792,6 +1840,7 @@ def setup_args():
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parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
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parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
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parser.add_argument("--tts-backend", default=default_arguments['tts-backend'], help="Specifies which TTS backend to use.")
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parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
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parser.add_argument("--whisper-backend", default=default_arguments['whisper-backend'], action='store_true', help="Picks which whisper backend to use (openai/whisper, lightmare/whispercpp)")
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parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
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@ -1828,10 +1877,48 @@ def setup_args():
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return args
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def get_default_settings( hypenated=True ):
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settings = {
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'listen': None if not args.listen else args.listen,
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'share': args.share,
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'low-vram':args.low_vram,
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'check-for-updates':args.check_for_updates,
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'models-from-local-only':args.models_from_local_only,
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'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
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'defer-tts-load': args.defer_tts_load,
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'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
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'device-override': args.device_override,
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'sample-batch-size': args.sample_batch_size,
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'embed-output-metadata': args.embed_output_metadata,
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'latents-lean-and-mean': args.latents_lean_and_mean,
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'voice-fixer': args.voice_fixer,
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'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
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'concurrency-count': args.concurrency_count,
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'output-sample-rate': args.output_sample_rate,
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'autocalculate-voice-chunk-duration-size': args.autocalculate_voice_chunk_duration_size,
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'output-volume': args.output_volume,
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'tts-backend': args.tts_backend,
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'autoregressive-model': args.autoregressive_model,
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'vocoder-model': args.vocoder_model,
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'whisper-backend': args.whisper_backend,
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'whisper-model': args.whisper_model,
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'training-default-halfp': args.training_default_halfp,
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'training-default-bnb': args.training_default_bnb,
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}
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res = {}
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for k in settings:
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res[k.replace("-", "_") if not hypenated else k] = settings[k]
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return res
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def update_args( **kwargs ):
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global args
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settings = {}
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settings = get_default_settings(hypenated=False)
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settings.update(kwargs)
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args.listen = settings['listen']
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@ -1853,8 +1940,10 @@ def update_args( **kwargs ):
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args.autocalculate_voice_chunk_duration_size = settings['autocalculate_voice_chunk_duration_size']
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args.output_volume = settings['output_volume']
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args.tts_backend = settings['tts_backend']
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args.autoregressive_model = settings['autoregressive_model']
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args.vocoder_model = settings['vocoder_model']
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args.whisper_backend = settings['whisper_backend']
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args.whisper_model = settings['whisper_model']
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@ -1865,34 +1954,7 @@ def update_args( **kwargs ):
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def save_args_settings():
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global args
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settings = {
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'listen': None if not args.listen else args.listen,
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'share': args.share,
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'low-vram':args.low_vram,
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'check-for-updates':args.check_for_updates,
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'models-from-local-only':args.models_from_local_only,
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'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
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'defer-tts-load': args.defer_tts_load,
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'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
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'device-override': args.device_override,
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'sample-batch-size': args.sample_batch_size,
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'embed-output-metadata': args.embed_output_metadata,
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'latents-lean-and-mean': args.latents_lean_and_mean,
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'voice-fixer': args.voice_fixer,
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'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
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'concurrency-count': args.concurrency_count,
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'output-sample-rate': args.output_sample_rate,
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'autocalculate-voice-chunk-duration-size': args.autocalculate_voice_chunk_duration_size,
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'output-volume': args.output_volume,
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'autoregressive-model': args.autoregressive_model,
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'vocoder-model': args.vocoder_model,
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'whisper-backend': args.whisper_backend,
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'whisper-model': args.whisper_model,
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'training-default-halfp': args.training_default_halfp,
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'training-default-bnb': args.training_default_bnb,
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}
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settings = get_default_settings()
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os.makedirs('./config/', exist_ok=True)
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with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
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@ -2009,18 +2071,13 @@ def load_tts( restart=False, autoregressive_model=None ):
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if autoregressive_model == "auto":
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autoregressive_model = deduce_autoregressive_model()
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print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")
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if get_device_name() == "cpu":
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print("!!!! WARNING !!!! No GPU available in PyTorch. You may need to reinstall PyTorch.")
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tts_loading = True
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try:
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, vocoder_model=args.vocoder_model)
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except Exception as e:
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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load_autoregressive_model(autoregressive_model)
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print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, vocoder_model=args.vocoder_model)
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tts_loading = False
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get_model_path('dvae.pth')
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@ -548,11 +548,11 @@ def setup_gradio():
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EXEC_SETTINGS['autocalculate_voice_chunk_duration_size'] = gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size)
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EXEC_SETTINGS['output_volume'] = gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume)
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# EXEC_SETTINGS['tts_backend'] = gr.Dropdown(TTSES, label="TTS Backend", value=args.tts_backend if args.tts_backend else TTSES[0])
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EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
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EXEC_SETTINGS['vocoder_model'] = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
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EXEC_SETTINGS['training_default_halfp'] = TRAINING_SETTINGS['half_p']
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EXEC_SETTINGS['training_default_bnb'] = TRAINING_SETTINGS['bitsandbytes']
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