import torch
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import json
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import torch.distributed as dist
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import numpy as np
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import kaldiio
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import librosa
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import torchaudio
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import time
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import logging
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from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank
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class IndexedDatasetJsonl(torch.utils.data.Dataset):
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def __init__(self, path):
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super().__init__()
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contents = []
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with open(path, encoding='utf-8') as fin:
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for line in fin:
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data = json.loads(line.strip())
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if "text" in data: # for sft
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self.contents.append(data['text'])
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if "source" in data: # for speech lab pretrain
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prompt = data["prompt"]
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source = data["source"]
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target = data["target"]
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source_len = data["source_len"]
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target_len = data["target_len"]
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contents.append({"source": source,
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"prompt": prompt,
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"target": target,
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"source_len": source_len,
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"target_len": target_len,
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}
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)
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self.contents = []
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total_num = len(contents)
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try:
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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except:
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rank = 0
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world_size = 1
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logging.warning("distributed is not initialized, only single shard")
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num_per_rank = total_num // world_size
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# rank = 0
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# import ipdb; ipdb.set_trace()
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self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]
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logging.info("in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(rank, len(self.contents), len(contents)))
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def __len__(self):
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return len(self.contents)
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def __getitem__(self, index):
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return self.contents[index]
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def get_source_len(self, data_dict):
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return data_dict["source_len"]
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def get_target_len(self, data_dict):
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return data_dict["target_len"] if "target_len" in data_dict else 0
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class AudioDataset(torch.utils.data.Dataset):
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def __init__(self, path, frontend=None, tokenizer=None, int_pad_value: int = -1, float_pad_value: float = 0.0, **kwargs):
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super().__init__()
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self.indexed_dataset = IndexedDatasetJsonl(path)
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self.frontend = frontend.forward
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self.fs = 16000 if frontend is None else frontend.fs
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self.data_type = "sound"
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self.tokenizer = tokenizer
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self.int_pad_value = int_pad_value
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self.float_pad_value = float_pad_value
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def __len__(self):
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return len(self.indexed_dataset)
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def __getitem__(self, index):
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item = self.indexed_dataset[index]
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source = item["source"]
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data_src = load_audio(source, fs=self.fs)
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speech, speech_lengths = extract_fbank(data_src, self.data_type, self.frontend) # speech: [b, T, d]
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target = item["target"]
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ids = self.tokenizer.encode(target)
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ids_lengths = len(ids)
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text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
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return {"speech": speech[0, :, :],
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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}
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def collator(self, samples: list=None):
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# return samples
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outputs = {}
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for sample in samples:
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for key in sample.keys():
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if data_list[0].dtype == torch.int64:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
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return outputs
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