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https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
Skip embedding (#950)
* Skip embedding * Skip earlier * Remove unused paramater * Pass param
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Vega
GitHub
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1862d2145b
commit
156723e37c
@ -39,6 +39,9 @@ data_info = {
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}
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}
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def should_skip(fpath: Path, skip_existing: bool) -> bool:
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return skip_existing and fpath.exists()
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def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
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skip_existing: bool, hparams, no_alignments: bool,
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dataset: str, emotion_extract = False, encoder_model_fpath=None):
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@ -99,7 +102,7 @@ def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
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print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
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print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
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def embed_utterance(fpaths, encoder_model_fpath):
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def _embed_utterance(fpaths: str, encoder_model_fpath: str):
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if not encoder.is_loaded():
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encoder.load_model(encoder_model_fpath)
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@ -110,15 +113,13 @@ def embed_utterance(fpaths, encoder_model_fpath):
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embed = encoder.embed_utterance(wav)
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np.save(embed_fpath, embed, allow_pickle=False)
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def _emo_extract_from_utterance(fpaths, hparams, skip_existing=False):
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if skip_existing and fpaths.exists():
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return
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def _emo_extract_from_utterance(fpaths, hparams):
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wav_fpath, emo_fpath = fpaths
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wav = np.load(wav_fpath)
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emo = extract_emo(np.expand_dims(wav, 0), hparams.sample_rate, True)
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np.save(emo_fpath, emo.squeeze(0), allow_pickle=False)
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def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_processes: int):
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def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_processes: int, skip_existing: bool):
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wav_dir = synthesizer_root.joinpath("audio")
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metadata_fpath = synthesizer_root.joinpath("train.txt")
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assert wav_dir.exists() and metadata_fpath.exists()
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@ -128,11 +129,11 @@ def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_proce
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# Gather the input wave filepath and the target output embed filepath
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with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
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metadata = [line.split("|") for line in metadata_file]
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fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
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fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata if not should_skip(embed_dir.joinpath(m[2]), skip_existing)]
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# TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
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# Embed the utterances in separate threads
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func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
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func = partial(_embed_utterance, encoder_model_fpath=encoder_model_fpath)
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job = Pool(n_processes).imap(func, fpaths)
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tuple(tqdm(job, "Embedding", len(fpaths), unit="utterances"))
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@ -142,14 +143,14 @@ def create_emo(synthesizer_root: Path, n_processes: int, skip_existing: bool, hp
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assert wav_dir.exists() and metadata_fpath.exists()
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emo_dir = synthesizer_root.joinpath("emo")
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emo_dir.mkdir(exist_ok=True)
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# Gather the input wave filepath and the target output embed filepath
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with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
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metadata = [line.split("|") for line in metadata_file]
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fpaths = [(wav_dir.joinpath(m[0]), emo_dir.joinpath(m[0].replace("audio-", "emo-"))) for m in metadata]
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fpaths = [(wav_dir.joinpath(m[0]), emo_dir.joinpath(m[0].replace("audio-", "emo-"))) for m in metadata if not should_skip(emo_dir.joinpath(m[0].replace("audio-", "emo-")), skip_existing)]
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# TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
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# Embed the utterances in separate threads
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func = partial(_emo_extract_from_utterance, hparams=hparams, skip_existing=skip_existing)
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func = partial(_emo_extract_from_utterance, hparams=hparams)
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job = Pool(n_processes).imap(func, fpaths)
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tuple(tqdm(job, "Emo", len(fpaths), unit="utterances"))
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@ -45,7 +45,7 @@ def extract_emo(
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return y
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def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
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skip_existing: bool, hparams, encoder_model_fpath):
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mel_fpath: str, wav_fpath: str, hparams, encoder_model_fpath):
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## FOR REFERENCE:
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# For you not to lose your head if you ever wish to change things here or implement your own
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# synthesizer.
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@ -58,13 +58,6 @@ def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
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# without extra padding. This means that you won't have an exact relation between the length
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# of the wav and of the mel spectrogram. See the vocoder data loader.
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# Skip existing utterances if needed
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mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
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wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
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if skip_existing and mel_fpath.exists() and wav_fpath.exists():
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return None
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# Trim silence
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if hparams.trim_silence:
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if not encoder.is_loaded():
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@ -112,50 +105,28 @@ def _split_on_silences(wav_fpath, words, hparams):
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def preprocess_general(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool, encoder_model_fpath: Path):
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metadata = []
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extensions = ("*.wav", "*.flac", "*.mp3")
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if skip_existing:
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for extension in extensions:
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wav_fpath_list = speaker_dir.glob(extension)
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# Iterate over each wav
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for wav_fpath in wav_fpath_list:
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words = dict_info.get(wav_fpath.name.split(".")[0])
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for extension in extensions:
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wav_fpath_list = speaker_dir.glob(extension)
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# Iterate over each wav
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for wav_fpath in wav_fpath_list:
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words = dict_info.get(wav_fpath.name.split(".")[0])
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if not words:
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words = dict_info.get(wav_fpath.name) # try with extension
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if not words:
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words = dict_info.get(wav_fpath.name) # try with extension
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if not words:
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print("no wordS")
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continue
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sub_basename = "%s_%02d" % (wav_fpath.name, 0)
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mel_fpath = out_dir.joinpath("mels", f"mel-{sub_basename}.npy")
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wav_fpath_ = out_dir.joinpath("audio", f"audio-{sub_basename}.npy")
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if mel_fpath.exists() and wav_fpath_.exists():
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print(f"No word found in dict_info for {wav_fpath.name}, skip it")
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continue
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sub_basename = "%s_%02d" % (wav_fpath.name, 0)
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mel_fpath = out_dir.joinpath("mels", f"mel-{sub_basename}.npy")
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wav_fpath = out_dir.joinpath("audio", f"audio-{sub_basename}.npy")
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if skip_existing and mel_fpath.exists() and wav_fpath.exists():
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continue
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wav, text = _split_on_silences(wav_fpath, words, hparams)
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result = _process_utterance(wav, text, out_dir, sub_basename,
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False, hparams, encoder_model_fpath) # accelarate
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if result is None:
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continue
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wav_fpath_name, mel_fpath_name, embed_fpath_name, wav, mel_frames, text = result
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metadata.append ((wav_fpath_name, mel_fpath_name, embed_fpath_name, len(wav), mel_frames, text))
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wav, text = _split_on_silences(wav_fpath, words, hparams)
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result = _process_utterance(wav, text, out_dir, sub_basename,
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False, hparams, encoder_model_fpath) # accelarate
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if result is None:
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continue
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wav_fpath_name, mel_fpath_name, embed_fpath_name, wav, mel_frames, text = result
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metadata.append ((wav_fpath_name, mel_fpath_name, embed_fpath_name, len(wav), mel_frames, text))
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else:
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for extension in extensions:
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wav_fpath_list = speaker_dir.glob(extension)
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# Iterate over each wav
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for wav_fpath in wav_fpath_list:
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words = dict_info.get(wav_fpath.name.split(".")[0])
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if not words:
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words = dict_info.get(wav_fpath.name) # try with extension
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if not words:
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print("no wordS")
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continue
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sub_basename = "%s_%02d" % (wav_fpath.name, 0)
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wav, text = _split_on_silences(wav_fpath, words, hparams)
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result = _process_utterance(wav, text, out_dir, sub_basename,
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False, hparams, encoder_model_fpath)
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if result is None:
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continue
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wav_fpath_name, mel_fpath_name, embed_fpath_name, wav, mel_frames, text = result
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metadata.append ((wav_fpath_name, mel_fpath_name, embed_fpath_name, len(wav), mel_frames, text))
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return metadata
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2
pre.py
2
pre.py
@ -71,7 +71,7 @@ if __name__ == "__main__":
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del args.n_processes_embed
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preprocess_dataset(**vars(args))
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create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath)
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create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath, skip_existing=args.skip_existing)
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if args.emotion_extract:
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create_emo(synthesizer_root=args.out_dir, n_processes=n_processes_embed, skip_existing=args.skip_existing, hparams=args.hparams)
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