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from torch.utils.data import Dataset
import json
import torchaudio
import os
from typing import Optional, Dict, Any, List, Tuple
import pandas as pd
import warnings
import random
from pathlib import Path
from collections import defaultdict
class TEARSDataset(Dataset):
"""
TEARS dataset class that loads audio and associated metadata/responses.
Args:
json_path (str): Path to the JSON file containing TEARS data
tears_root (str): Root directory containing TEARS audio files
sample_rate (int, optional): Target sample rate for audio. Defaults to 16000.
duration (float, optional): Target duration in seconds. Defaults to 3.0.
normalize_audio (bool, optional): Whether to normalize audio. Defaults to True.
Returns:
Dict containing:
- audio_tensor: torch.Tensor of shape (1, num_samples)
- speaker_id: str, speaker identifier
- metadata: dict containing speaker metadata
- prompt: str, randomly selected prompt
- response: str, corresponding response
- filepath: str, path to audio file
"""
def __init__(
self,
json_path: str,
tears_root: str,
sample_rate: int = 16000,
duration: float = 3.0,
normalize_audio: bool = True,
augment: bool = True
):
super().__init__()
# Load the JSON data
with open(json_path, 'r') as f:
self.data = json.load(f)
self.tears_root = Path(tears_root)
self.sample_rate = sample_rate
self.duration = duration
self.normalize_audio = normalize_audio
self.target_samples = int(duration * sample_rate)
self.augment = augment
def __len__(self) -> int:
return len(self.data)
def augment_audio(self, waveform, sample_rate):
# Randomly select augmentation methods
augmentation_choices = ['time_stretch', 'pitch_shift', 'add_noise', 'spec_aug']
random.shuffle(augmentation_choices)
for aug in augmentation_choices[:random.randint(1, len(augmentation_choices))]:
if aug == 'time_stretch':
rate = random.uniform(0.8, 1.25)
effect = [['speed', str(rate)], ['rate', str(16000)]]
waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
waveform, 16000, effects=effect
)
elif aug == 'pitch_shift':
n_steps = random.randint(-4, 4)
effect = [['pitch', str(n)] for n in [n_steps*100 for n in [random.choice([-2, -1, 1, 2])]]]
waveform, _ = torchaudio.sox_effects.apply_effects_tensor(waveform, 16000, effect)
elif aug == 'add_noise':
noise = torch.randn_like(waveform) * random.uniform(0.001, 0.015)
waveform = waveform + noise
elif aug == 'frequency_mask':
freq_mask = T.FrequencyMasking(freq_mask_param=random.randint(15, 30))
waveform = freq_mask(waveform)
elif aug == 'time_mask':
time_mask = T.TimeMasking(time_mask_param=random.randint(20, 80))
waveform = time_mask(waveform)
elif aug == 'reverb':
effect = [['reverb', '-w', str(random.randint(10, 50))]]
waveform, _ = torchaudio.sox_effects.apply_effects_tensor(waveform, 16000, effect)
elif aug == 'pitch_shift':
steps = random.randint(-2, 2)
effect = [['pitch', str(steps * 100)], ['rate', '16000']]
waveform, _ = torchaudio.sox_effects.apply_effects_tensor(waveform, 16000, effect)
return waveform
def __getitem__(self, idx: int) -> Dict[str, Any]:
# Get sample data
sample = self.data[idx]
# Get file path
audio_path = str(self.tears_root / sample['audio_path'])
# Load and process audio
try:
audio, sr = torchaudio.load(audio_path)
# Resample if necessary
if sr != self.sample_rate:
audio = torchaudio.transforms.Resample(sr, self.sample_rate)(audio)
if self.augment:
audio = self.augment_audio(audio, self.sample_rate)
# Normalize if requested
if self.normalize_audio:
mean = torch.mean(audio)
std = torch.std(audio)
audio = (audio - mean) / (std + 1e-8)
# Handle duration
num_samples = audio.shape[1]
if num_samples >= self.target_samples:
# Randomly crop to target duration
start_sample = random.randint(0, num_samples - self.target_samples)
audio = audio[:, start_sample:start_sample + self.target_samples]
else:
# Pad if shorter than target duration
pad_size = self.target_samples - num_samples
audio = torch.nn.functional.pad(audio, (0, pad_size))
except Exception as e:
warnings.warn(f"Error loading audio file {audio_path}: {str(e)}")
# Return zero tensor if audio loading fails
audio = torch.zeros(1, self.target_samples)
# Get prompt and response
prompts = sample.get('prompts', [])
responses = sample.get('responses', [])
if prompts and responses and len(prompts) == len(responses):
rand_idx = random.randint(0, len(prompts) - 1)
prompt = prompts[rand_idx]
response = responses[rand_idx].replace("\n", " ").strip()
else:
prompt = None
response = None
return {
'audio_tensor': audio,
'sid': sample['speaker']['id'],
'metadata': sample['speaker'],
'prompt': prompt,
'answer': response,
'filename': str(audio_path)
}
@staticmethod
def redistribute_speakers(
json_paths: Dict[str, str],
split_ratios: Dict[str, float],
seed: int = 42
) -> Dict[str, List[Dict]]:
"""
Redistribute speakers across splits according to given ratios.
Args:
json_paths: Dict mapping split names to json file paths
split_ratios: Dict mapping split names to desired ratios (should sum to 1)
seed: Random seed for reproducibility
Returns:
Dict mapping split names to lists of samples
"""
random.seed(seed)
# Collect all samples and group by speaker
speaker_samples = defaultdict(list)
for split, path in json_paths.items():
with open(path, 'r') as f:
data = json.load(f)
for sample in data:
speaker_samples[sample['speaker']['id']].append(sample)
# Get list of all speakers
all_speakers = list(speaker_samples.keys())
random.shuffle(all_speakers)
# Calculate number of speakers for each split
total_speakers = len(all_speakers)
split_speakers = {
split: int(ratio * total_speakers)
for split, ratio in split_ratios.items()
}
# Adjust for rounding errors
remainder = total_speakers - sum(split_speakers.values())
if remainder > 0:
# Add remaining speakers to first split
split_speakers[list(split_speakers.keys())[0]] += remainder
# Distribute speakers to splits
new_splits = defaultdict(list)
current_idx = 0
for split, num_speakers in split_speakers.items():
split_speaker_ids = all_speakers[current_idx:current_idx + num_speakers]
for speaker_id in split_speaker_ids:
new_splits[split].extend(speaker_samples[speaker_id])
current_idx += num_speakers
return new_splits
@staticmethod
def save_splits(splits: Dict[str, List[Dict]], output_dir: str):
"""Save redistributed splits to JSON files."""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for split_name, samples in splits.items():
output_path = output_dir / f"tears_dataset_{split_name}_with_responses.json"
with open(output_path, 'w') as f:
json.dump(samples, f, indent=2)
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