From adc88bd9e76644badbbe006913addfa7cbe5d89c Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期四, 23 十一月 2023 20:40:15 +0800
Subject: [PATCH] Merge remote-tracking branch 'refs/remotes/origin/main' update contextual forward
---
funasr/datasets/dataset_jsonl.py | 124 +++++++++++++++++++++++++++++++++++++++++
1 files changed, 124 insertions(+), 0 deletions(-)
diff --git a/funasr/datasets/dataset_jsonl.py b/funasr/datasets/dataset_jsonl.py
new file mode 100644
index 0000000..72d9a99
--- /dev/null
+++ b/funasr/datasets/dataset_jsonl.py
@@ -0,0 +1,124 @@
+import torch
+import json
+import torch.distributed as dist
+import numpy as np
+import kaldiio
+import librosa
+
+
+
+def load_audio(audio_path: str, fs: int=16000):
+ audio = None
+ if audio_path.startswith("oss:"):
+ pass
+ elif audio_path.startswith("odps:"):
+ pass
+ else:
+ if ".ark:" in audio_path:
+ audio = kaldiio.load_mat(audio_path)
+ else:
+ audio, fs = librosa.load(audio_path, sr=fs)
+ return audio
+
+def extract_features(data, date_type: str="sound", frontend=None):
+ if date_type == "sound":
+ feat, feats_lens = frontend(data, len(data))
+ feat = feat[0, :, :]
+ else:
+ feat, feats_lens = torch.from_numpy(data).to(torch.float32), torch.tensor([data.shape[0]]).to(torch.int32)
+ return feat, feats_lens
+
+
+
+class IndexedDatasetJsonl(torch.utils.data.Dataset):
+
+ def __init__(self, path):
+ super().__init__()
+ # data_parallel_size = dist.get_world_size()
+ data_parallel_size = 1
+ contents = []
+ with open(path, encoding='utf-8') as fin:
+ for line in fin:
+ data = json.loads(line.strip())
+ if "text" in data: # for sft
+ self.contents.append(data['text'])
+ if "source" in data: # for speech lab pretrain
+ prompt = data["prompt"]
+ source = data["source"]
+ target = data["target"]
+ source_len = data["source_len"]
+ target_len = data["target_len"]
+
+ contents.append({"source": source,
+ "prompt": prompt,
+ "target": target,
+ "source_len": source_len,
+ "target_len": target_len,
+ }
+ )
+
+ self.contents = []
+ total_num = len(contents)
+ num_per_rank = total_num // data_parallel_size
+ # rank = dist.get_rank()
+ rank = 0
+ # import ipdb; ipdb.set_trace()
+ self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]
+
+
+ def __len__(self):
+ return len(self.contents)
+
+ def __getitem__(self, index):
+ return self.contents[index]
+
+
+class AudioDataset(torch.utils.data.Dataset):
+ def __init__(self, path, frontend=None, tokenizer=None):
+ super().__init__()
+ self.indexed_dataset = IndexedDatasetJsonl(path)
+ self.frontend = frontend.forward
+ self.fs = 16000 if frontend is None else frontend.fs
+ self.data_type = "sound"
+ self.tokenizer = tokenizer
+ self.int_pad_value = -1
+ self.float_pad_value = 0.0
+
+
+
+
+ def __len__(self):
+ return len(self.indexed_dataset)
+
+ def __getitem__(self, index):
+ item = self.indexed_dataset[index]
+ source = item["source"]
+ data_src = load_audio(source, fs=self.fs)
+ speech, speech_lengths = extract_features(data_src, self.data_type, self.frontend)
+ target = item["target"]
+ text = self.tokenizer.encode(target)
+ text_lengths = len(text)
+ text, text_lengths = torch.tensor(text, dtype=torch.int64), torch.tensor([text_lengths], dtype=torch.int32)
+ return {"speech": speech,
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ }
+
+
+ def collator(self, samples: list=None):
+
+ outputs = {}
+ for sample in samples:
+ for key in sample.keys():
+ if key not in outputs:
+ outputs[key] = []
+ outputs[key].append(sample[key])
+
+ for key, data_list in outputs.items():
+ if data_list[0].dtype.kind == "i":
+ pad_value = self.int_pad_value
+ else:
+ pad_value = self.float_pad_value
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
+ return samples
\ No newline at end of file
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