From 22b928dd3ff37ccee57ab2b5c2e4fcda4d33d24d Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期四, 05 十二月 2024 19:30:30 +0800
Subject: [PATCH] Merge pull request #2269 from modelscope/dev_sx2
---
funasr/utils/load_utils.py | 33 +++++++++++++++++++++++++++++++--
1 files changed, 31 insertions(+), 2 deletions(-)
diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index faafc0f..9613d1b 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -1,6 +1,7 @@
import os
import torch
import json
+from io import BytesIO
import torch.distributed as dist
import numpy as np
import kaldiio
@@ -9,6 +10,7 @@
import time
import logging
from torch.nn.utils.rnn import pad_sequence
+from pydub import AudioSegment
try:
from funasr.download.file import download_from_url
@@ -76,7 +78,7 @@
for audio in data_or_path_or_list
]
if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith(
- ("http://", "https://")
+ ("http://", "https://")
): # download url to local file
data_or_path_or_list = download_from_url(data_or_path_or_list)
@@ -112,7 +114,7 @@
elif isinstance(data_or_path_or_list, str) and data_type == "text" and tokenizer is not None:
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
- data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
+ data_or_path_or_list = torch.from_numpy(data_or_path_or_list) # .squeeze() # [n_samples,]
elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark":
data_mat = kaldiio.load_mat(data_or_path_or_list)
if isinstance(data_mat, tuple):
@@ -136,6 +138,7 @@
def load_bytes(input):
+ # input = validate_frame_rate(input)
middle_data = np.frombuffer(input, dtype=np.int16)
middle_data = np.asarray(middle_data)
if middle_data.dtype.kind not in "iu":
@@ -151,6 +154,32 @@
return array
+def validate_frame_rate(
+ input,
+ fs: int = 16000,
+):
+
+ # 灏嗘枃浠惰鍙栦负瀛楄妭娴�
+ byte_data = BytesIO(input)
+
+ # 浣跨敤 pydub 鍔犺浇闊抽
+ audio = AudioSegment.from_file(byte_data)
+
+ # 纭繚閲囨牱鐜囦负 16000 Hz
+ if audio.frame_rate != fs:
+ audio = audio.set_frame_rate(fs)
+
+ # 灏嗛噸鏂伴噰鏍峰悗鐨勯煶棰戝鍑轰负瀛楄妭娴�
+ output = BytesIO()
+ audio.export(output, format="wav")
+ output.seek(0)
+
+ # 鑾峰彇閲嶆柊閲囨牱鍚庣殑瀛楄妭娴佹暟鎹�
+ input = output.read()
+
+ return input
+
+
def extract_fbank(data, data_len=None, data_type: str = "sound", frontend=None, **kwargs):
if isinstance(data, np.ndarray):
data = torch.from_numpy(data)
--
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