mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
124 lines
3.3 KiB
Python
124 lines
3.3 KiB
Python
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 |