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import io
import base64
import librosa
import numpy as np
import math
import torch
import torchaudio
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
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