From 27f31cd42bb4e20dc19de0034fc0d80b449f1db1 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 06 十二月 2023 17:01:12 +0800
Subject: [PATCH] funasr2

---
 funasr/datasets/dataset_jsonl.py |   26 ++++++++++++++++----------
 1 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/funasr/datasets/dataset_jsonl.py b/funasr/datasets/dataset_jsonl.py
index ec2bf07..eef67c5 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
@@ -17,7 +17,9 @@
 		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):
@@ -76,21 +78,26 @@
 	
 	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):
-	def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=None):
-
+	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
-		self.token_id_converter = token_id_converter
 
-		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
 
 	
 
@@ -106,8 +113,7 @@
 		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 = self.tokenizer.encode(target)
 		ids_lengths = len(ids)
 		text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
 

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