85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
import numpy as np
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import re
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import unicodedata
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from fairseq import checkpoint_utils
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from fairseq.data import Dictionary
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import torch
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import logging
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logging.getLogger("fairseq").setLevel(logging.WARNING)
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import sys
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import os
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import numpy as np
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import asyncio
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import ffmpeg
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from pathlib import Path
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import subprocess
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# Get the parent directory of the current file
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this_dir = Path(__file__).resolve().parent.parent.parent.parent.parent
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def load_audio(file, sampling_rate):
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try:
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file = str(file).strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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try:
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# Use ffmpeg-python
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stream = (
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ffmpeg
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.input(file)
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.output('pipe:', format='f32le', acodec='pcm_f32le', ac=1, ar=str(sampling_rate))
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.run(capture_stdout=True, capture_stderr=True)
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)
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out = stream[0] # Get stdout data
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except ffmpeg.Error as e:
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print(f"FFmpeg error: {e.stderr.decode('utf-8')}")
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raise RuntimeError(f"FFmpeg error: {e.stderr.decode('utf-8')}") from e
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return np.frombuffer(out, np.float32).flatten()
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except Exception as error:
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print(f"Error loading audio: {error}")
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raise RuntimeError(f"Failed to load audio: {error}") from error
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def format_title(title):
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formatted_title = (
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unicodedata.normalize("NFKD", title).encode("ascii", "ignore").decode("utf-8")
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)
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formatted_title = re.sub(r"[\u2500-\u257F]+", "", formatted_title)
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formatted_title = re.sub(r"[^\w\s.-]", "", formatted_title)
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formatted_title = re.sub(r"\s+", "_", formatted_title)
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return formatted_title
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def load_embedding(embedder_model):
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#print("EMBEDDER MODEL IS", embedder_model)
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embedding_list = {
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"contentvec": "contentvec_base.pt",
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"hubert": "hubert_base.pt",
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}
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try:
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this_dir = Path(__file__).resolve().parent.parent.parent.parent.parent
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model_path = this_dir / "models" / "rvc_base" / embedding_list[embedder_model]
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#print("MODEL PATH IS", model_path)
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# Load model ensemble and task
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torch.serialization.add_safe_globals([Dictionary])
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models = checkpoint_utils.load_model_ensemble_and_task(
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[f"{model_path}"],
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suffix="",
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)
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#print(f"Embedding model {embedder_model} loaded successfully.")
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return models
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except KeyError as e:
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logging.error(f"Invalid embedder model name: {embedder_model}")
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raise ValueError(f"Invalid embedder model name: {embedder_model}") from e
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except Exception as e:
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logging.error(f"Error loading embedding model: {e}")
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raise RuntimeError(f"Error loading embedding model: {e}") from e
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