133 lines
5.5 KiB
Python
133 lines
5.5 KiB
Python
import librosa
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import numpy as np
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from models.encoder import inference as encoder
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from utils import logmmse
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from models.synthesizer import audio
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from pathlib import Path
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from pypinyin import Style
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from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
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from pypinyin.converter import DefaultConverter
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from pypinyin.core import Pinyin
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import torch
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from transformers import Wav2Vec2Processor
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from .models.wav2emo import EmotionExtractorModel
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class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
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pass
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pinyin = Pinyin(PinyinConverter()).pinyin
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# load model from hub
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device = 'cuda' if torch.cuda.is_available() else "cpu"
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model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
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processor = Wav2Vec2Processor.from_pretrained(model_name)
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model = EmotionExtractorModel.from_pretrained(model_name).to(device)
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def extract_emo(
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x: np.ndarray,
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sampling_rate: int,
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embeddings: bool = False,
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) -> np.ndarray:
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r"""Predict emotions or extract embeddings from raw audio signal."""
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y = processor(x, sampling_rate=sampling_rate)
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y = y['input_values'][0]
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y = torch.from_numpy(y).to(device)
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# run through model
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with torch.no_grad():
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y = model(y)[0 if embeddings else 1]
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# convert to numpy
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y = y.detach().cpu().numpy()
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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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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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# - Both the audios and the mel spectrograms are saved as numpy arrays
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# - There is no processing done to the audios that will be saved to disk beyond volume
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# normalization (in split_on_silences)
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# - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This
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# is why we re-apply it on the audio on the side of the vocoder.
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# - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
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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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# Trim silence
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if hparams.trim_silence:
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if not encoder.is_loaded():
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encoder.load_model(encoder_model_fpath)
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wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True)
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# Skip utterances that are too short
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if len(wav) < hparams.utterance_min_duration * hparams.sample_rate:
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return None
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# Compute the mel spectrogram
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mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
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mel_frames = mel_spectrogram.shape[1]
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# Skip utterances that are too long
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if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
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return None
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# Write the spectrogram, embed and audio to disk
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np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
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np.save(wav_fpath, wav, allow_pickle=False)
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# Return a tuple describing this training example
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return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, wav, mel_frames, text
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def _split_on_silences(wav_fpath, words, hparams):
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# Load the audio waveform
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wav, _ = librosa.load(wav_fpath, sr= hparams.sample_rate)
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wav = librosa.effects.trim(wav, top_db= 40, frame_length=2048, hop_length=1024)[0]
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if hparams.rescale:
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wav = wav / np.abs(wav).max() * hparams.rescaling_max
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# denoise, we may not need it here.
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if len(wav) > hparams.sample_rate*(0.3+0.1):
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noise_wav = np.concatenate([wav[:int(hparams.sample_rate*0.15)],
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wav[-int(hparams.sample_rate*0.15):]])
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profile = logmmse.profile_noise(noise_wav, hparams.sample_rate)
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wav = logmmse.denoise(wav, profile, eta=0)
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resp = pinyin(words, style=Style.TONE3)
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res = filter(lambda v : not v.isspace(),map(lambda v: v[0],resp))
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res = " ".join(res)
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return wav, res
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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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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(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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return metadata
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