python - Speech to Text - 将演讲者标签映射到 JSON 响应中的相应成绩单
问题描述
每隔一段时间就会出现一段 JSON 数据，它提出了一个挑战，可能需要几个小时才能从中提取所需的信息。我有以下从 Speech To Text API 引擎生成的 JSON 响应。
speaker 0它显示了每个说话者和对话中的记录、每个单词的话语以及时间戳和说话者标签speaker 2。
{
"results": [
{
"alternatives": [
{
"timestamps": [
[
"the",
6.18,
6.63
],
[
"weather",
6.63,
6.95
],
[
"is",
6.95,
7.53
],
[
"sunny",
7.73,
8.11
],
[
"it's",
8.21,
8.5
],
[
"time",
8.5,
8.66
],
[
"to",
8.66,
8.81
],
[
"sip",
8.81,
8.99
],
[
"in",
8.99,
9.02
],
[
"some",
9.02,
9.25
],
[
"cold",
9.25,
9.32
],
[
"beer",
9.32,
9.68
]
],
"confidence": 0.812,
"transcript": "the weather is sunny it's time to sip in some cold beer "
}
],
"final": "True"
},
{
"alternatives": [
{
"timestamps": [
[
"sure",
10.52,
10.88
],
[
"that",
10.92,
11.19
],
[
"sounds",
11.68,
11.82
],
[
"like",
11.82,
12.11
],
[
"a",
12.32,
12.96
],
[
"plan",
12.99,
13.8
]
],
"confidence": 0.829,
"transcript": "sure that sounds like a plan"
}
],
"final": "True"
}
],
"result_index":0,
"speaker_labels": [
{
"from": 6.18,
"to": 6.63,
"speaker": 0,
"confidence": 0.475,
"final": "False"
},
{
"from": 6.63,
"to": 6.95,
"speaker": 0,
"confidence": 0.475,
"final": "False"
},
{
"from": 6.95,
"to": 7.53,
"speaker": 0,
"confidence": 0.475,
"final": "False"
},
{
"from": 7.73,
"to": 8.11,
"speaker": 0,
"confidence": 0.499,
"final": "False"
},
{
"from": 8.21,
"to": 8.5,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 8.5,
"to": 8.66,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 8.66,
"to": 8.81,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 8.81,
"to": 8.99,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 8.99,
"to": 9.02,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 9.02,
"to": 9.25,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 9.25,
"to": 9.32,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 9.32,
"to": 9.68,
"speaker": 0,
"confidence": 0.472,
"final": "False"
},
{
"from": 10.52,
"to": 10.88,
"speaker": 2,
"confidence": 0.441,
"final": "False"
},
{
"from": 10.92,
"to": 11.19,
"speaker": 2,
"confidence": 0.364,
"final": "False"
},
{
"from": 11.68,
"to": 11.82,
"speaker": 2,
"confidence": 0.372,
"final": "False"
},
{
"from": 11.82,
"to": 12.11,
"speaker": 2,
"confidence": 0.372,
"final": "False"
},
{
"from": 12.32,
"to": 12.96,
"speaker": 2,
"confidence": 0.383,
"final": "False"
},
{
"from": 12.99,
"to": 13.8,
"speaker": 2,
"confidence": 0.428,
"final": "False"
}
]
}
原谅缩进问题（如果有的话），但 JSON 是有效的，我一直在尝试将每个成绩单与其相应的扬声器标签映射。
我想要像下面这样的东西。上面的 JSON 大约有 20,000 行，它是一场噩梦，它根据时间戳和单词话语提取说话者标签并将其与transcript.
[
{
"transcript": "the weather is sunny it's time to sip in some cold beer ",
"speaker" : 0
},
{
"transcript": "sure that sounds like a plan",
"speaker" : 2
}
]
到目前为止我已经尝试过：JSON 数据存储在一个名为example.json. 我已经能够将每个单词及其相应的时间戳和说话者标签放在一个元组列表中（见下面的输出）：
import json
# with open('C:\\Users\\%USERPROFILE%\\Desktop\\example.json', 'r') as f:
# data = json.load(f)
l1 = []
l2 = []
l3 = []
for i in data['results']:
for j in i['alternatives'][0]['timestamps']:
l1.append(j)
for m in data['speaker_labels']:
l2.append(m)
for q in l1:
for n in l2:
if q[1]==n['from']:
l3.append((q[0],n['speaker'], q[1], q[2]))
print(l3)
这给出了输出：
[('the', 0, 6.18, 6.63),
('weather', 0, 6.63, 6.95),
('is', 0, 6.95, 7.53),
('sunny', 0, 7.73, 8.11),
("it's", 0, 8.21, 8.5),
('time', 0, 8.5, 8.66),
('to', 0, 8.66, 8.81),
('sip', 0, 8.81, 8.99),
('in', 0, 8.99, 9.02),
('some', 0, 9.02, 9.25),
('cold', 0, 9.25, 9.32),
('beer', 0, 9.32, 9.68),
('sure', 2, 10.52, 10.88),
('that', 2, 10.92, 11.19),
('sounds', 2, 11.68, 11.82),
('like', 2, 11.82, 12.11),
('a', 2, 12.32, 12.96),
('plan', 2, 12.99, 13.8)]
但是现在我不确定如何根据时间戳比较将单词关联在一起，并“存储”每组单词以再次使用其说话者标签形成成绩单。
我还设法在列表中获取了成绩单，但现在如何从上述列表中提取每个成绩单的说话者标签。不幸的是，演讲者为每个单词贴上了标签speaker 0，speaker 2我希望他们会为每个单词贴上标签transcript。
for i in data['results']:
l4.append(i['alternatives'][0]['transcript'])
这给出了输出：
["the weather is sunny it's time to sip in some cold beer ",'sure that sounds like a plan']
我已尽我所能解释这个问题，但我愿意接受任何反馈，如有必要，我会做出改变。另外，我很确定有更好的方法来解决这个问题，而不是列出几个列表，非常感谢任何帮助。
对于更大的数据集，请参阅pastebin。我希望这个数据集可以对性能基准测试有所帮助。我可以在可用时或需要时提供更大的数据集。
当我处理大型 JSON 数据时，性能是一个重要因素，同样在重叠转录中准确实现扬声器隔离是另一个要求。
解决方案
使用 pandas，这就是我刚才处理它的方法。
假设数据存储在一个名为data
import pandas as pd
labels = pd.DataFrame.from_records(data['speaker_labels'])
transcript_tstamps = pd.DataFrame.from_records(
[t for r in data['results']
for a in r['alternatives']
for t in a['timestamps']],
columns=['word', 'from', 'to']
)
# this list comprehension more-efficiently de-nests the dictionary into
# records that can be used to create a DataFrame
df = labels.merge(transcript_tstamps)
# produces a dataframe of speakers to words based on timestamps from & to
# since I knew I wanted to merge on the from & to columns,
# I named the columns thus when I created the transcript_tstamps data frame
# like this:
confidence final from speaker to word
0 0.475 False 6.18 0 6.63 the
1 0.475 False 6.63 0 6.95 weather
2 0.475 False 6.95 0 7.53 is
3 0.499 False 7.73 0 8.11 sunny
4 0.472 False 8.21 0 8.50 it's
5 0.472 False 8.50 0 8.66 time
6 0.472 False 8.66 0 8.81 to
7 0.472 False 8.81 0 8.99 sip
8 0.472 False 8.99 0 9.02 in
9 0.472 False 9.02 0 9.25 some
10 0.472 False 9.25 0 9.32 cold
11 0.472 False 9.32 0 9.68 beer
12 0.441 False 10.52 2 10.88 sure
13 0.364 False 10.92 2 11.19 that
14 0.372 False 11.68 2 11.82 sounds
15 0.372 False 11.82 2 12.11 like
16 0.383 False 12.32 2 12.96 a
17 0.428 False 12.99 2 13.80 plan
说话人和单词数据合并后，需要将同一说话人的连续单词分组在一起，以得出当前说话人。例如，如果扬声器阵列看起来像 [2,2,2,2,0,0,0,2,2,2,0,0,0,0]，我们需要将前四个2组合在一起，然后接下来的三个0，然后是三个2，然后是剩下的0。
对数据进行排序['from', 'to']，然后为此设置一个虚拟变量，current_speaker如下所示：
df = df.sort_values(['from', 'to'])
df['current_speaker'] = (df.speaker.shift() != df.speaker).cumsum()
从这里，按 分组current_speaker，将单词聚合成一个句子并转换为 json。有一些额外的重命名来修复输出 json 键
transcripts = df.groupby('current_speaker').agg({
'word': lambda x: ' '.join(x),
'speaker': min
}).rename(columns={'word': 'transcript'})
transcripts[['speaker', 'transcript']].to_json(orient='records')
# produces the following output (indentation added by me for legibility):
'[{"speaker":0,
"transcript":"the weather is sunny it\'s time to sip in some cold beer"},
{"speaker":2,
"transcript":"sure that sounds like a plan"}]'
要在脚本开始/结束时添加其他数据，您可以将 from/to 的 min/max 添加到 groupby
transcripts = df.groupby('current_speaker').agg({
'word': lambda x: ' '.join(x),
'speaker': min,
'from': min,
'to': max
}).rename(columns={'word': 'transcript'})
此外，（尽管这不适用于此示例数据集）您也许应该为每个时间片选择具有最高置信度的替代方案。
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