From 7012ca2efc130103c4acd24e3678c7ae280f8db4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 十二月 2023 20:08:55 +0800
Subject: [PATCH] funasr2 paraformer biciparaformer contextuaparaformer
---
funasr/datasets/dataset_jsonl.py | 108 +++++++++++++++++------------------------------------
1 files changed, 35 insertions(+), 73 deletions(-)
diff --git a/funasr/datasets/dataset_jsonl.py b/funasr/datasets/dataset_jsonl.py
index 1b20c95..21df89e 100644
--- a/funasr/datasets/dataset_jsonl.py
+++ b/funasr/datasets/dataset_jsonl.py
@@ -4,36 +4,11 @@
import numpy as np
import kaldiio
import librosa
+import torchaudio
+import time
+import logging
-
-
-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":
-<<<<<<< HEAD
- 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, feats_lens = frontend(data, len(data))
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
- 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
+from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank
@@ -41,8 +16,7 @@
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:
@@ -66,38 +40,46 @@
self.contents = []
total_num = len(contents)
- num_per_rank = total_num // data_parallel_size
- # rank = dist.get_rank()
- rank = 0
+ try:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ except:
+ rank = 0
+ world_size = 1
+ logging.warning("distributed is not initialized, only single shard")
+ num_per_rank = total_num // world_size
+
+ # rank = 0
# import ipdb; ipdb.set_trace()
self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]
-
+
+ logging.info("in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(rank, len(self.contents), len(contents)))
def __len__(self):
return len(self.contents)
def __getitem__(self, index):
return self.contents[index]
+
+ def get_source_len(self, data_dict):
+ return data_dict["source_len"]
+
+ def get_target_len(self, data_dict):
+
+ return data_dict["target_len"] if "target_len" in data_dict else 0
class AudioDataset(torch.utils.data.Dataset):
-<<<<<<< HEAD
- def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=None):
-=======
- def __init__(self, path, frontend=None, tokenizer=None):
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
+ def __init__(self, path, frontend=None, tokenizer=None, int_pad_value: int = -1, float_pad_value: float = 0.0, **kwargs):
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
-<<<<<<< HEAD
- self.token_id_converter = token_id_converter
-=======
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
- self.int_pad_value = -1
- self.float_pad_value = 0.0
+
+ self.int_pad_value = int_pad_value
+ self.float_pad_value = float_pad_value
@@ -107,25 +89,16 @@
def __getitem__(self, index):
item = self.indexed_dataset[index]
-<<<<<<< HEAD
- # return item
-=======
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
+
source = item["source"]
data_src = load_audio(source, fs=self.fs)
- speech, speech_lengths = extract_features(data_src, self.data_type, self.frontend)
+ speech, speech_lengths = extract_fbank(data_src, self.data_type, self.frontend) # speech: [b, T, d]
target = item["target"]
-<<<<<<< HEAD
- text = self.tokenizer.text2tokens(target)
- ids = self.token_id_converter.tokens2ids(text)
+ ids = self.tokenizer.encode(target)
ids_lengths = len(ids)
text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
-=======
- 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)
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
- return {"speech": speech,
+
+ return {"speech": speech[0, :, :],
"speech_lengths": speech_lengths,
"text": text,
"text_lengths": text_lengths,
@@ -134,32 +107,21 @@
def collator(self, samples: list=None):
-<<<<<<< HEAD
# return samples
-=======
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
outputs = {}
for sample in samples:
for key in sample.keys():
if key not in outputs:
outputs[key] = []
outputs[key].append(sample[key])
-<<<<<<< HEAD
for key, data_list in outputs.items():
if data_list[0].dtype == torch.int64:
-=======
-
- for key, data_list in outputs.items():
- if data_list[0].dtype.kind == "i":
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
+
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)
-<<<<<<< HEAD
return outputs
-=======
- return samples
->>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
+
--
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