utils.py

import io
import base64

import librosa
import numpy as np
import math
import os
import threading
import torch
import torchaudio
import sox
import tempfile


def encode_wav(wav, sr, rep_format="wav"):
    with io.BytesIO() as wavio:
        torchaudio.save(wavio, wav, sr, format=rep_format)
        audio_bytes = wavio.getvalue()
        encoded_wav = base64.b64encode(audio_bytes).decode("ascii")
    return encoded_wav


def trim_silence(audio, sr, keep_left_time=0.05, keep_right_time=0.22, hop_size=240):
    _, index = librosa.effects.trim(audio, top_db=20, frame_length=512, hop_length=128)
    num_frames = int(math.ceil((index[1] - index[0]) / hop_size))  # 300

    left_sil_samples = int(keep_left_time * sr)
    right_sil_samples = int(keep_right_time * sr)

    wav_len = len(audio)
    start_idx = index[0] - left_sil_samples
    trim_wav = audio

    if start_idx > 0:
        trim_wav = trim_wav[start_idx:]
    else:
        trim_wav = np.pad(
            trim_wav, (abs(start_idx), 0), mode="constant", constant_values=0.0
        )
    wav_len = len(trim_wav)
    out_len = int(num_frames * hop_size + (keep_left_time + keep_right_time) * sr)

    if out_len < wav_len:
        trim_wav = trim_wav[:out_len]
    else:
        trim_wav = np.pad(
            trim_wav, (0, (out_len - wav_len)), mode="constant", constant_values=0.0
        )
    return trim_wav


def volumn_adjust(audio16bit_torch, sr, volumn_ratio):
    """使用sox进行音频音量调整
    Args:
        audio16bit_torch (Tensor): 输入音频张量 [1, samples]
        volume_ratio (float): 音量比率，>1增大音量，<1降低音量

    Returns:
        Tensor: 调整音量后的音频张量
    """
    # 创建临时文件
    with tempfile.NamedTemporaryFile(
        suffix=".wav", delete=True
    ) as temp_in, tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_out:
        # 保存输入音频到临时文件
        torchaudio.save(temp_in.name, audio16bit_torch, sr)  # 假设采样率为16000
        # 创建sox转换器
        tfm = sox.Transformer()
        tfm.vol(volumn_ratio)  # 设置音量调整比率
        # 应用音量调整
        tfm.build_file(temp_in.name, temp_out.name)
        # 读取处理后的音频
        audio_changed, _ = torchaudio.load(temp_out.name)
    return audio_changed


def speech_adjust(audio16bit_torch, sr, speed_ratio):
    """使用sox进行音频变速处理
    Args:
        audio16bit_torch (Tensor): 输入音频张量 [1, samples]
        speed_ratio (float): 速度比率，>1加速，<1减速

    Returns:
        Tensor: 变速后的音频张量
    """
    # 创建临时文件
    with tempfile.NamedTemporaryFile(
        suffix=".wav", delete=True
    ) as temp_in, tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_out:
        # 保存输入音频到临时文件
        torchaudio.save(temp_in.name, audio16bit_torch, sr)  # 假设采样率为16000
        # 创建sox转换器
        tfm = sox.Transformer()
        tfm.tempo(speed_ratio)  # 设置变速比率
        # 应用变速处理
        tfm.build_file(temp_in.name, temp_out.name)
        # 读取处理后的音频
        audio_changed, _ = torchaudio.load(temp_out.name)
    return audio_changed


def audio_resample(audio16bit_torch, result_sr, target_sample_rate):
    audio16bit_torch = torchaudio.transforms.Resample(
        orig_freq=result_sr, new_freq=target_sample_rate
    )(audio16bit_torch)
    result_sr = target_sample_rate
    return audio16bit_torch, result_sr


def norm_audio(audio16bit_torch):
    # 直接 归一化处理。
    audio16bit_torch = audio16bit_torch.numpy()
    audio16bit_torch = (
        audio16bit_torch / np.abs(audio16bit_torch).max() * 32767
    ).astype(np.int16)
    audio16bit_torch = torch.from_numpy(audio16bit_torch)
    return audio16bit_torch


def resample_audio(wav, original_sample_rate, target_sample_rate):
    if original_sample_rate != target_sample_rate:
        assert (
            original_sample_rate > target_sample_rate
        ), "wav sample rate {} must be greater than {}".format(
            original_sample_rate, target_sample_rate
        )
        wav = torchaudio.transforms.Resample(
            orig_freq=original_sample_rate, new_freq=target_sample_rate
        )(wav)
    return wav


def energy_norm_fn(wav):
    if type(wav) is np.ndarray:
        max_data = np.max(np.abs(wav))
        wav = wav / max(max_data, 0.01) * 0.999
    else:
        max_data = torch.max(torch.abs(wav))
        wav = wav / max(max_data, 0.01) * 0.999
    return wav


def get_audio_tokens(audio_tokens: str) -> list[int]:
    audio_tokens = audio_tokens.split("><audio_")
    audio_tokens = [
        int(token.replace("<audio_", "").replace(">", "")) + 65536
        for token in audio_tokens
    ]
    return audio_tokens


def load_audio(audio_path: str):
    audio_wav, sr = torchaudio.load(audio_path)
    audio_wav = audio_wav.mean(dim=0, keepdim=True)
    return audio_wav, sr


# load optimus_ths for flash attention, make sure LD_LIBRARY_PATH has `nvidia/cuda_nvrtc/lib`
# if not, please manually set LD_LIBRARY_PATH=xxx/python3.10/site-packages/nvidia/cuda_nvrtc/lib
def load_optimus_ths_lib(libpath):
    if not hasattr(load_optimus_ths_lib, 'lock'):
        load_optimus_ths_lib.lock = threading.Lock()
    if not hasattr(load_optimus_ths_lib, 'success'):
        load_optimus_ths_lib.success = False

    with load_optimus_ths_lib.lock:
        if load_optimus_ths_lib.success:
            return load_optimus_ths_lib.success

        try:
            if torch.__version__ >= "2.5":
                torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so'))
            elif torch.__version__ >= "2.3":
                torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.3-cu121.cpython-310-x86_64-linux-gnu.so'))
            elif torch.__version__ >= "2.2":
                torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.2-cu121.cpython-310-x86_64-linux-gnu.so'))
            else:
                raise RuntimeError("Unsupported torch version")
            print("Load optimus_ths successfully and flash attn would be enabled")
            load_optimus_ths_lib.success = True
        except Exception as err:
            print(f"Fail to load optimus_ths and flash attn is disabled: {err}")
            load_optimus_ths_lib.success = False

        return load_optimus_ths_lib.success