mirror of
https://github.com/babysor/Realtime-Voice-Clone-Chinese.git
synced 2026-05-09 07:32:59 +08:00
101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
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import os
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import torch
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import numpy as np
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from tqdm import tqdm
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from pathlib import Path
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from ppg_extractor import load_model
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import encoder.inference as Encoder
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from encoder.audio import preprocess_wav
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from encoder import audio
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from utils.f0_utils import compute_f0
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from torch.multiprocessing import Pool, cpu_count
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from functools import partial
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SAMPLE_RATE=16000
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def _compute_bnf(
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wav: any,
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output_fpath: str,
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device: torch.device,
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ppg_model_local: any,
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):
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"""
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Compute CTC-Attention Seq2seq ASR encoder bottle-neck features (BNF).
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"""
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ppg_model_local.to(device)
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wav_tensor = torch.from_numpy(wav).float().to(device).unsqueeze(0)
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wav_length = torch.LongTensor([wav.shape[0]]).to(device)
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with torch.no_grad():
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bnf = ppg_model_local(wav_tensor, wav_length)
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bnf_npy = bnf.squeeze(0).cpu().numpy()
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np.save(output_fpath, bnf_npy, allow_pickle=False)
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def _compute_f0_from_wav(wav, output_fpath):
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"""Compute merged f0 values."""
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f0 = compute_f0(wav, SAMPLE_RATE)
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np.save(output_fpath, f0, allow_pickle=False)
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def _compute_spkEmbed(wav, output_fpath, encoder_model_local, device):
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Encoder.set_model(encoder_model_local)
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# Compute where to split the utterance into partials and pad if necessary
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wave_slices, mel_slices = Encoder.compute_partial_slices(len(wav), rate=1.3, min_pad_coverage=0.75)
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max_wave_length = wave_slices[-1].stop
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if max_wave_length >= len(wav):
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wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
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# Split the utterance into partials
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frames = audio.wav_to_mel_spectrogram(wav)
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frames_batch = np.array([frames[s] for s in mel_slices])
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partial_embeds = Encoder.embed_frames_batch(frames_batch)
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# Compute the utterance embedding from the partial embeddings
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raw_embed = np.mean(partial_embeds, axis=0)
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embed = raw_embed / np.linalg.norm(raw_embed, 2)
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np.save(output_fpath, embed, allow_pickle=False)
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def preprocess_one(wav_path, out_dir, device, ppg_model_local, encoder_model_local):
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wav = preprocess_wav(wav_path, 24000, False, False)
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utt_id = os.path.basename(wav_path).rstrip(".wav")
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_compute_bnf(output_fpath=f"{out_dir}/bnf/{utt_id}.ling_feat.npy", wav=wav, device=device, ppg_model_local=ppg_model_local)
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_compute_f0_from_wav(output_fpath=f"{out_dir}/f0/{utt_id}.f0.npy", wav=wav)
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_compute_spkEmbed(output_fpath=f"{out_dir}/embed/{utt_id}.npy", device=device, encoder_model_local=encoder_model_local, wav=wav)
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def preprocess_dataset(datasets_root, dataset, out_dir, n_processes, ppg_encoder_model_fpath, speaker_encoder_model):
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# Glob wav files
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wav_file_list = sorted(Path(f"{datasets_root}/{dataset}").glob("**/*.wav"))
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print(f"Globbed {len(wav_file_list)} wav files.")
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out_dir.joinpath("bnf").mkdir(exist_ok=True, parents=True)
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out_dir.joinpath("f0").mkdir(exist_ok=True, parents=True)
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out_dir.joinpath("embed").mkdir(exist_ok=True, parents=True)
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# ppg_model_local = load_model(ppg_encoder_model_fpath, "cpu")
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ppg_model_local = None
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encoder_model_local = Encoder.load_model(speaker_encoder_model, "cpu")
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if n_processes is None:
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n_processes = cpu_count()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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func = partial(preprocess_one, out_dir=out_dir, ppg_model_local=ppg_model_local, encoder_model_local=encoder_model_local, device=device)
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job = Pool(n_processes).imap(func, wav_file_list)
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list(tqdm(job, "Preprocessing", len(wav_file_list), unit="wav"))
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t_fid_file = out_dir.joinpath("train_fidlist.txt").open("w", encoding="utf-8")
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d_fid_file = out_dir.joinpath("dev_fidlist.txt").open("w", encoding="utf-8")
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e_fid_file = out_dir.joinpath("eval_fidlist.txt").open("w", encoding="utf-8")
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for file in wav_file_list:
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id = os.path.basename(file).rstrip(".wav")
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if id.endswith("1"):
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d_fid_file.write(id + "\n")
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elif id.endswith("9"):
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e_fid_file.write(id + "\n")
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else:
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t_fid_file.write(id + "\n")
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t_fid_file.close()
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d_fid_file.close()
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e_fid_file.close()
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