From b5d3df75cf6462aa3bf42fd3c86fa2aa7f1c8a15 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 24 十一月 2023 00:54:44 +0800
Subject: [PATCH] setup jamo
---
funasr/datasets/dataset_jsonl.py | 107 +++++++++++++++++++++++++++++++++++++++++++++++++++--
1 files changed, 103 insertions(+), 4 deletions(-)
diff --git a/funasr/datasets/dataset_jsonl.py b/funasr/datasets/dataset_jsonl.py
index 283fbd9..543b60e 100644
--- a/funasr/datasets/dataset_jsonl.py
+++ b/funasr/datasets/dataset_jsonl.py
@@ -1,12 +1,48 @@
import torch
import json
import torch.distributed as dist
+import numpy as np
+import kaldiio
+import librosa
+import torchaudio
+import time
-class AudioDatasetJsonl(torch.utils.data.Dataset):
+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)
+ audio, fs = torchaudio.load(audio_path)
+ audio = audio[0, :]
+ return audio
+
+def extract_features(data, date_type: str="sound", frontend=None):
+ if date_type == "sound":
+
+ if isinstance(data, np.ndarray):
+ data = torch.from_numpy(data).to(torch.float32)
+ data_len = torch.tensor([data.shape[0]]).to(torch.int32)
+ feat, feats_lens = frontend(data[None, :], data_len)
+
+ 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
- def __init__(self, path, data_parallel_rank=0, data_parallel_size=1):
+
+
+class IndexedDatasetJsonl(torch.utils.data.Dataset):
+
+ def __init__(self, path):
super().__init__()
- data_parallel_size = dist.get_world_size()
+ # data_parallel_size = dist.get_world_size()
+ data_parallel_size = 1
contents = []
with open(path, encoding='utf-8') as fin:
for line in fin:
@@ -31,7 +67,8 @@
self.contents = []
total_num = len(contents)
num_per_rank = total_num // data_parallel_size
- rank = dist.get_rank()
+ # rank = dist.get_rank()
+ rank = 0
# import ipdb; ipdb.set_trace()
self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]
@@ -41,3 +78,65 @@
def __getitem__(self, index):
return self.contents[index]
+
+
+class AudioDataset(torch.utils.data.Dataset):
+ def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=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.token_id_converter = token_id_converter
+
+ 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]
+ # return item
+
+ 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.text2tokens(target)
+ ids = self.token_id_converter.tokens2ids(text)
+ ids_lengths = len(ids)
+ text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
+
+ return {"speech": speech,
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ }
+
+
+ def collator(self, samples: list=None):
+
+ # return samples
+
+ 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 == torch.int64:
+
+ 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 outputs
+
--
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