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 | 18 ++++++++++++++----
1 files changed, 14 insertions(+), 4 deletions(-)
diff --git a/funasr/datasets/dataset_jsonl.py b/funasr/datasets/dataset_jsonl.py
index 9e4ee6f..543b60e 100644
--- a/funasr/datasets/dataset_jsonl.py
+++ b/funasr/datasets/dataset_jsonl.py
@@ -4,8 +4,8 @@
import numpy as np
import kaldiio
import librosa
-
-
+import torchaudio
+import time
def load_audio(audio_path: str, fs: int=16000):
audio = None
@@ -17,15 +17,19 @@
if ".ark:" in audio_path:
audio = kaldiio.load_mat(audio_path)
else:
- audio, fs = librosa.load(audio_path, sr=fs)
+ # 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)
@@ -78,6 +82,7 @@
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
@@ -85,6 +90,7 @@
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
@@ -97,6 +103,7 @@
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)
@@ -105,6 +112,7 @@
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,
@@ -125,8 +133,10 @@
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
\ No newline at end of file
+ return outputs
+
--
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