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# Copyright (c) 2024 NVIDIA CORPORATION.
# Licensed under the MIT license.
import functools
import io
import json
import math
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false" # disable the tokenizer parallelism warning
import random
import re
import string
import subprocess
import sys
import yaml
import numpy as np
from collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass
from functools import partial
from pydub import AudioSegment
from tqdm import tqdm
import torch
import torchvision
from torch.utils.data import DataLoader, Dataset, get_worker_info
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoTokenizer
import librosa
import soundfile as sf
def int16_to_float32(x):
return (x / 32767.0).astype(np.float32)
def float32_to_int16(x):
x = np.clip(x, a_min=-1., a_max=1.)
return (x * 32767.).astype(np.int16)
class AudioTextDataProcessor:
def __init__(
self,
data_root: str,
clap_config: dict,
tokenizer,
max_tokens: int,
**kwargs
):
self.data_root = data_root
self.clap_config = clap_config
self.tokenizer = tokenizer
self.tokenizer.padding_side = "right"
self.max_tokens = max_tokens
def get_num_windows(self, T, sr):
clap_config = self.clap_config
window_length = int(float(clap_config["window_length"]) * sr)
window_overlap = int(float(clap_config["window_overlap"]) * sr)
max_num_window = int(clap_config["max_num_window"])
num_windows = 1
if T <= window_length:
num_windows = 1
full_length = window_length
elif T >= (max_num_window * window_length - (max_num_window - 1) * window_overlap):
num_windows = max_num_window
full_length = (max_num_window * window_length - (max_num_window - 1) * window_overlap)
else:
num_windows = 1 + int(np.ceil((T - window_length) / float(window_length - window_overlap)))
full_length = num_windows * window_length - (num_windows - 1) * window_overlap
return num_windows, full_length
def load_audio(self, file_path, target_sr=44100, duration=30.0, start=0.0):
if file_path.endswith('.mp3'):
audio = AudioSegment.from_file(file_path)
if len(audio) > (start + duration) * 1000:
audio = audio[start * 1000:(start + duration) * 1000]
if audio.frame_rate != target_sr:
audio = audio.set_frame_rate(target_sr)
if audio.channels > 1:
audio = audio.set_channels(1)
data = np.array(audio.get_array_of_samples())
if audio.sample_width == 2:
data = data.astype(np.float32) / np.iinfo(np.int16).max
elif audio.sample_width == 4:
data = data.astype(np.float32) / np.iinfo(np.int32).max
else:
raise ValueError("Unsupported bit depth: {}".format(audio.sample_width))
else:
with sf.SoundFile(file_path) as audio:
original_sr = audio.samplerate
channels = audio.channels
max_frames = int((start + duration) * original_sr)
audio.seek(int(start * original_sr))
frames_to_read = min(max_frames, len(audio))
data = audio.read(frames_to_read)
if data.max() > 1 or data.min() < -1:
data = data / max(abs(data.max()), abs(data.min()))
if original_sr != target_sr:
if channels == 1:
data = librosa.resample(data.flatten(), orig_sr=original_sr, target_sr=target_sr)
else:
data = librosa.resample(data.T, orig_sr=original_sr, target_sr=target_sr)[0]
else:
if channels != 1:
data = data.T[0]
if data.min() >= 0:
data = 2 * data / abs(data.max()) - 1.0
else:
data = data / max(abs(data.max()), abs(data.min()))
assert len(data.shape) == 1, data.shape
return data
def compute_sliding_window(self, audio_file, audio_start=0.0):
if type(audio_start) == str:
audio_start = float(audio_start)
clap_config = self.clap_config
if clap_config["method"] == 'laion-clap':
sr = 48000
elif clap_config["method"] == 'microsoft-clap':
sr = 44100
else:
raise NotImplementedError
window_length = int(float(clap_config["window_length"]) * sr)
window_overlap = int(float(clap_config["window_overlap"]) * sr)
max_num_window = int(clap_config["max_num_window"])
duration = max_num_window * (clap_config["window_length"] - clap_config["window_overlap"]) + clap_config["window_overlap"]
audio_data = self.load_audio(audio_file, sr, duration, audio_start)
T = len(audio_data)
num_windows, full_length = self.get_num_windows(T, sr)
if full_length > T:
audio_data = np.append(audio_data, np.zeros(full_length - T))
audio_data = audio_data.reshape(1, -1)
audio_data_tensor = torch.from_numpy(int16_to_float32(float32_to_int16(audio_data))).float()
audio_clips = []
audio_embed_mask = torch.zeros(max_num_window)
for i in range(num_windows):
start = i * (window_length - window_overlap)
audio_clips.append(audio_data_tensor[:, start:start+window_length])
audio_embed_mask[i] = 1
assert sum(audio_embed_mask) == num_windows
if num_windows < max_num_window:
for _ in range(max_num_window - num_windows):
audio_clips.append(torch.zeros_like(audio_clips[-1]))
audio_clips = torch.cat(audio_clips) # (max_num_window, window_length * sr) cuda tensor
return audio_clips, audio_embed_mask
def preprocess_string_for_eval(self, x):
x = x.rstrip().lstrip()
x = x.lower()
return x
def process(self, item):
if type(item['name']) is str:
audio_files = [os.path.join(self.data_root, item['name'])]
audio_starts = [0 if 'audio_start' not in item else float(item['audio_start'])]
else:
audio_files = [os.path.join(self.data_root, name) for name in item['name']]
audio_starts = [0] * len(audio_files) if 'audio_start' not in item else item['audio_start']
audio_clips, audio_embed_mask = [], []
for audio_file, audio_start in zip(audio_files, audio_starts):
this_audio_clips, this_audio_embed_mask = self.compute_sliding_window(audio_file, audio_start)
audio_clips.append(this_audio_clips)
audio_embed_mask.append(this_audio_embed_mask)
audio_clips = torch.cat(audio_clips)
audio_embed_mask = torch.cat(audio_embed_mask)
correct_num_windows = int(self.clap_config["max_num_window"]) * int(self.clap_config["max_num_fewshot"])
if len(audio_clips) < correct_num_windows:
audio_clips = torch.cat([
audio_clips,
torch.zeros(correct_num_windows - len(audio_clips), audio_clips.shape[1])
])
audio_embed_mask = torch.cat([
audio_embed_mask,
torch.zeros(correct_num_windows - len(audio_embed_mask))
])
audio_clips.requires_grad = False
audio_embed_mask.requires_grad = False
assert type(item['name']) is str
# simple data - 1 audio, 1 text
if 'prompt' in item:
text_prompt = item['prompt'].lower()
prefix = item['prefix'].lower() # the task is xxx.
sample = "{}{} <audio>{}\nanswer:{}".format(
self.tokenizer.bos_token,
self.preprocess_string_for_eval(prefix),
self.preprocess_string_for_eval(text_prompt),
self.tokenizer.sep_token
)
# dialog data - 1 audio, multiple text
elif 'dialogue' in item:
dialogue = item['dialogue']
prefix = item['prefix'].lower() # the task is dialog.
sample = f"{self.tokenizer.bos_token}{prefix}<audio>"
for each_round in dialogue:
sample = sample + f"user: {each_round['user']} \nassistant: {self.tokenizer.sep_token}"
if 'assistant' in each_round:
sample = sample + f"{each_round['assistant']}<|endofchunk|>{self.tokenizer.eos_token}\n"
text = self.tokenizer(
sample,
max_length=self.max_tokens*5,
padding="longest",
truncation="only_first",
return_tensors="pt"
)
return (item['name'], audio_clips, audio_embed_mask, text["input_ids"], text["attention_mask"])
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