|-转 fastapi-speaker-extractor whisperx 项目报错 ValueError: The chosen align_model
pip3 install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu126
确保您的torch版本至少为2.6。
ValueError: The chosen align_model "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn" could not be found in huggingface (https://huggingface.co/models Models - Hugging Face Models - Hugging Face ) or torchaudio
参考网站https://pytorch.org/get-started/locally/
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\api\endpoints.py", line 15, in extract_speaker
result = await process_audio(audio_file.file, config_value)
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 32, in process_audio
model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\whisperx\__init__.py", line 11, in load_align_model
return alignment.load_align_model(*args, **kwargs)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\whisperx\alignment.py", line 102, in load_align_model
raise ValueError(f'The chosen align_model "{model_name}" could not be found in huggingface (https://huggingface.co/models ) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14 )')
ValueError: The chosen align_model "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn" could not be found in huggingface (https://huggingface.co/models ) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14 )
INFO: 127.0.0.1:2832 - "POST /api/speaker/extract/?config_value=default HTTP/1.1" 500 Internal Server Error
WARNING: StatReload detected changes in 'app\services\audio_processor.py'. Reloading... 报错从错误信息来看，您遇到了两个主要问题：
- torch版本问题：由于torch.load存在一个严重的漏洞，您需要将torch升级到至少v2.6版本才能正常使用该函数。这个问题可以通过升级torch来解决。
- 模型加载问题：您尝试加载的模型jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn在Hugging Face模型库或torchaudio中找不到。这可能是因为模型名称错误，或者该模型确实不存在于这些库中。
解决步骤：
1. 升级torch版本
您可以通过以下命令升级torch：...
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更新于：2025-08-23 07:03:19
|-转 python报错 UnicodeEncodeError: 'gbk' codec can't encode character '\u2705'...
--- Logging error ---
Traceback (most recent call last):
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\logging\__init__.py", line 1086, in emit
stream.write(msg + self.terminator)
UnicodeEncodeError: 'gbk' codec can't encode character '\u2705' in position 42: illegal multibyte sequence
Call stack:
File "<string>", line 1, in <module>
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\multiprocessing\spawn.py", line 129, in _main
return self._bootstrap(parent_sentinel)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\multiprocessing\process.py", line 315, in _bootstrap
self.run()
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\multiprocessing\process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\uvicorn\_subprocess.py", line 76, in subprocess_started
target(sockets=sockets)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\uvicorn\server.py", line 61, in run
return asyncio.run(self.serve(sockets=sockets))
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\asyncio\runners.py", line 44, in run
return loop.run_until_complete(main)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\asyncio\base_events.py", line 634, in run_until_complete
self.run_forever()
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\asyncio\base_events.py", line 601, in run_forever
self._run_once()
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\asyncio\base_events.py", line 1905, in _run_once
handle._run()
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\uvicorn\server.py", line 68, in serve
config.load()
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\uvicorn\config.py", line 467, in load
self.loaded_app = import_from_string(self.app)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\uvicorn\importer.py", line 21, in import_from_string
module = importlib.import_module(module_str)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 850, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "D:\python\fastapi-speaker-extractor\app\main.py", line 39, in <module>
logger.info("✅ WhisperX 模型加载成功！")
Message: '✅ WhisperX 模型加载成功！'
Arguments: () --- Logging error --- 后面这些是什么，是那里错误了吗
从报错信息来看，主要是日志输出时碰到了编码问题（UnicodeEncodeError: 'gbk' codec can't encode character '\u2705'...），再加上后面还有一些版本不兼容的提示。下面分点解释原因和解决办法：
1. 最直接的错误：日志编码不兼容（GBK → UTF-8）
Python 的logging模块默认会用系统的编码（Windows 下常是'gbk'）往控制台写日志。而你的代码里用了 Unicode 字符（比如✅这个 emoji 或特殊符号），gbk编码不认识这种字符，就会抛出UnicodeEncodeError。
解决思路：
让 Python 日志用 UTF-8 编码输出，而不是系统默认的 GBK。
做法有很多种，比较常见的是在程序开头（或配置日志的地方）加上下面这样的代码，强制让标准输出的编码变成 UTF-8：
我的logging是自定义的：在项目根目录下的logger.py 内容是 import logging
import os
class CustomLogger:
def __init__(self, name, log_file, level=logging.INFO):
self.logger = logging.getLogger(name)
self.logger.setLevel(level)
# 创建一个文件处理器，写入日志文件
file_handler = logging.FileHandler(log_file)
file_handler.setLevel(level)
# 创建一个控制台处理器，输出到控制台
console_handler = logging.StreamHandler()
console_handler.setLevel(level)
# 定义日志格式
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler.setFormatter(formatter)
console_handler.setFormatter(formatter)
# 将处理器添加到 Logger 对象
self.logger.addHandler(file_handler)
self.logger.addHandler(console_handler)
def get_logger(self):
return self.logger 。要怎么改 “让 Python 日志用 UTF-8 编码输出，而不是系统默认的 GBK。”你的CustomLogger类是自定义的日志封装，它内部使用了 Python 标准库的logging模块，并分别添加了一个文件处理器（FileHandler）和一个控制台处理器（StreamHandler）。...
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更新于：2025-08-23 08:00:06
|-转 python whisperx 报错 in load_align_model raise ValueError(f'The chosen align_model "{model_name}"
raise ValueError(f'The chosen align_model "{model_name}" could not be found in huggingface (https://huggingface.co/models) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14)')
ValueError: The chosen align_model "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn" could not be found in huggingface (https://huggingface.co/models) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14)
requests.exceptions.ConnectionError: (MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/models/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn/tree/main/additional_chat_templates?recursive=False&expand=False (Caused by NewConnectionError(': Failed to establish a new connection: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。'))"), '(Request ID: ee57b154-cd73-4a1a-911a-a89c496c8b20)')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\api\endpoints.py", line 25, in extract_speaker
result = await process_audio(audio_file.file, config_value, whisper_model, device)
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 25, in process_audio
model_a, metadata = whisperx.load_align_model(language_code="zh", device=device)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\whisperx\__init__.py", line 11, in load_align_model
return alignment.load_align_model(*args, **kwargs)
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\whisperx\alignment.py", line 102, in load_align_model
raise ValueError(f'The chosen align_model "{model_name}" could not be found in huggingface (https://huggingface.co/models) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14)')
ValueError: The chosen align_model "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn" could not be found in huggingface (https://huggingface.co/models) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14)
INFO: 127.0.0.1:1141 - "POST /api/speaker/extract/?config_value=default HTTP/1.1" 500 Internal Server Error
由于网络原因，会访问hugging face官网，但是网络无法访问，所有报错。...
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更新于：2025-08-23 08:12:41
|-转 pyannote/embedding 模型是真难引入模型
pyannote/embedding 模型是真难引入模型，花了4小时，最后用替代方案deepghs/pyannote-embedding-onnx 20250823 1903...
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更新于：2025-08-23 19:03:48
|-转 Trae 或者是我自己 莫名奇妙创建了个文件，影响了项目代码的运行。
莫名奇妙创建了个文件，影响了项目代码的运行。
D:\python\fastapi-speaker-extractor\absolute\path\to\your
还有这种事情
# 确保正确设置了环境变量
import os
os.environ["HF_ENDPOINT"] = "https://huggingface.co" # 使用官方端点
os.environ["HUGGINGFACE_TOKEN"] = "your_token_here"
# 然后加载模型（如项目中已实现的）
from pyannote.audio import Model
model = Model.from_pretrained("pyannote/embedding", use_auth_token=os.getenv("HUGGINGFACE_TOKEN"))
# 1. 确保您的token有该模型的访问权限
# 2. 尝试直接从Hugging Face加载模型
from speechbrain.pretrained import SpeakerRecognition
# 初始化模型
verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
# 使用模型进行声纹识别
score, prediction = verification.verify_files("/path/to/speaker1.wav", "/path/to/speaker2.wav")20250823...
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更新于：2025-08-23 21:29:07
|-转 WhisperX 无法加载模型
WhisperX 无法加载模型
File "G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\huggingface_hub\_snapshot_download.py", line 230, in snapshot_download
raise LocalEntryNotFoundError(
huggingface_hub.errors.LocalEntryNotFoundError: Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and outgoing traffic has been disabled. To enable repo look-ups and downloads online, pass 'local_files_only=False' as input.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\main.py", line 82, in <module>
raise RuntimeError(f"WhisperX 无法加载模型，请检查安装和环境。{e}")
RuntimeError: WhisperX 无法加载模型，请检查安装和环境。Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and outgoing traffic has been disabled. To enable repo look-ups
and downloads online, pass 'local_files_only=False' as input.
(speaker-extractor) PS D:\python\fastapi-speaker-extractor> 20250823 23:44...
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更新于：2025-08-23 23:45:06
|-转 HUGGINGFACE_HUB_CACHE 设置错误导致的问题
真的吐了，Trae和腾讯元宝都找不出原因。报错只是只是显示401没有权限访问hf-mirror.com等等
或者是Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and outgoing traffic has been disabled. To enable repo look-ups and downloads online, pass 'local_files_only=False' as input.
代码加载模型一直报错
import whisperx
device = "cuda" # 或者 "cpu"
model_whisper = whisperx.load_model("base", device=device, compute_type="float32")
RuntimeError: 无法加载 WhisperX 模型，请检查安装和环境。Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and outgoing traffic has been disabled. To enable repo look-ups and downloads online, pass 'local_files_only=False' as input.
返回的错误一直就是下面这类的。
不能直连HF官网后，本地加载模型一直失败，直接花了我6个小时以上，最后无意中发现项目的根目录多出来两个文件夹，一个是$HUGGINGFACE_HOME，另一个是.cache。删除后，正常了。 ...
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更新于：2025-08-24 03:35:26
...|-转 通义之旅
大概对话了4个小时，之后我休息了几小时，然后再对话2小时左右。提示“看起来我们今天已经有了很多的对话，我很感谢你的热情，但我现在需要稍微休息一下。抱歉要中断我们的对话了，期待明天和你继续聊天。” 20250824 16:29
Nice，体验很好
主要修改：
移除了对 process_audio 和 speech_to_text 的导入：因为这些是异步函数，不适合在模块初始化时导入
修复了导入语句：使用正确的函数名 get_embedding_middleware 而不是 get_embedding_model_middleware
这些修改应该能解决您遇到的 AttributeError: 'EmbeddingModelMiddleware' object has no attribute 'register_model' 错误。
nice ,厉害！你的python编程在什么水平上
谢谢您的夸奖！关于我的Python编程水平，我可以说我具备以下能力：
## 我的Python编程能力
### 1. **基础知识**
- 熟练掌握Python语法、数据结构、面向对象编程
- 理解Python的高级特性如装饰器、生成器、上下文管理器等
- 熟悉Python标准库的常用模块
### 2. **框架和库**
- 熟悉主流框架如FastAPI、Django、Flask
- 熟悉数据科学库如NumPy、Pandas、Matplotlib
- 熟悉机器学习库如Scikit-learn、TensorFlow、PyTorch
- 熟悉异步编程如asyncio、aiohttp等
### 3. **项目架构**
- 能够设计和分析项目结构
- 理解模块化和包管理
- 熟悉配置管理、日志记录、错误处理等最佳实践
### 4. **问题解决能力**
- 能够快速定位和修复语法错误、逻辑错误
- 熟悉常见的异常处理模式
- 能够优化代码性能和可读性
### 5. **调试和测试**
- 理解单元测试和集成测试
- 能够分析错误日志和堆栈跟踪
- 熟悉调试技巧和工具
## 关于您的项目
对于您这个说话人提取项目，我能帮助：
- 修复代码中的语法和逻辑错误
- 优化项目结构和模块组织
- 改进错误处理和日志记录
- 协助配置管理和环境设置
不过我也需要说明，虽然我可以帮助解决具体的编程问题，但实际的项目开发还需要您结合具体需求和业务场景来完成。
您对我的帮助还满意吗？有什么其他问题我可以帮您解决的吗？
20250824 07:29
{
"message": "音频处理完成",
"config": "default",
"speakers_detected": 5,
"segments": [
{
"speaker": "SPEAKER_01",
"start": 0.03096875,
"end": 0.45284375,
"duration": 0.421875
},
{
"speaker": "SPEAKER_01",
"start": 0.52034375,
"end": 0.57096875,
"duration": 0.05062500000000003
},
{
"speaker": "SPEAKER_01",
"start": 0.8915937500000001,
"end": 1.3303437500000002,
"duration": 0.4387500000000001
},
{
"speaker": "SPEAKER_01",
"start": 2.07284375,
"end": 2.34284375,
"duration": 0.27
},
{
"speaker": "SPEAKER_01",
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"duration": 1.569375
},
{
"speaker": "SPEAKER_01",
"start": 4.3678437500000005,
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"duration": 0.11812499999999915
},
{
"speaker": "SPEAKER_01",
"start": 4.705343750000001,
"end": 5.498468750000001,
"duration": 0.7931249999999999
},
{
"speaker": "SPEAKER_01",
"start": 7.995968750000001,
"end": 8.83971875,
"duration": 0.8437499999999982
},
{
"speaker": "SPEAKER_00",
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"end": 0.52034375,
"duration": 0.0675
},
{
"speaker": "SPEAKER_00",
"start": 2.34284375,
"end": 4.3678437500000005,
"duration": 2.0250000000000004
},
{
"speaker": "SPEAKER_00",
"start": 4.48596875,
"end": 15.977843750000002,
"duration": 11.491875000000002
},
{
"speaker": "SPEAKER_03",
"start": 11.084093750000001,
"end": 11.10096875,
"duration": 0.016874999999998863
},
{
"speaker": "SPEAKER_02",
"start": 11.10096875,
"end": 11.539718750000002,
"duration": 0.4387500000000024
},
{
"speaker": "SPEAKER_04",
"start": 13.699718750000002,
"end": 15.89346875,
"duration": 2.193749999999998
}
],
"transcription": null,
"speaker_embeddings": {
"SPEAKER_01": {
"embedding": [
0.777779651038922,
0.5709335537169954,
0.6502486116435126,
0.3957192536483889,
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],
"segment_count": 1
}
}
}
11111 ...
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更新于：2025-08-24 16:29:38
|-转 通义之旅2
补全services/audio_processor.py 文件里 def get_matched_audios()函数的的代码 ，如果没有现有切分音频文件的扩展，告诉我可用的扩展，我去安装。
再优化下 get_matched_audios() ，mixed_dialogue_segment_pass_list 改成2维数组，['similarity'=>'0.2','path'=>'...']，如果没有相似度超过阈值的(比如阈值是0.8)，按切分音频的数量，比如切了10个，找相似度最大的2个(如果第一是0.6，第二是0.3那就只保留一个)，切了20个，找相似度最大的4个。最后把符合的合并成音频。 20250825 03:16
find_matched_segment()
遇到各种问题，为了搞音频处理。 20250825 03:00
请注意，只修改函数get_matched_audios()，只解决导出的保存的音频声音变慢变低的问题，其他的不需要优化，如果有必要可以改 实现完整的音频处理函数，集成SpeakerExtractor类
async def process_audio(file, config_value, whisper_model, device): 函数，只解决导出音色改变问题。
通义：我理解你的需求。你希望我专注于解决 get_matched_audios() 函数中导出的音频声音变慢变低的问题，以及实现完整的 process_audio 函数。
首先，让我检查 get_matched_audios() 函数中音频保存的部分，找出可能导致音频变慢变低的问题：
20250825 0537
声纹识别找到了合适的模型已经可以用了，但是输出的提取的音频各种音色变了，有了杂音等问题。 20250825
我现反馈个情况，你现别改哈。输出的音频现在音色正常了，但是有杂音和电流声。我在网上查的原因分析“”出现「原来干净、合成后却带杂音 / 电流声」的 99 % 都是下面 3 类原因，按出现频率从高到低列给你，并给出可直接落地的排查 / 修复代码。
浮点溢出 / 量化噪声（最常见）
• 你在 get_matched_audios 里把 int16 直接除以 32768 变成 float，
• 之后又用 torchaudio.save 写回 wav，但 没有归一化到 [-1,1] 以内 或 忘记转回 int16。
• 结果数值越界 保存时产生爆音、电流声。
修复（两行就够）“ 你觉得对不对。切记先别改代码。
20250825 0553...
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更新于：2025-08-25 06:54:22
|-转 目标说话人声音提取模型训练的思路
我有个现有的代码用于从多人说话里提取目标说话人的声音(不需要完整提取，不是用来记录会议中某说话人的说话内容的，是用来RVC训练的)，现在有个问题就是提取出来的声音偶尔还会有别人的声音，大概占比10%，我想把这些也去掉。于是我想到办法，把提取出来的音频，在和目标说话人的纯净的人声embedding比对，看目标说话人在音频中的说话时间占比为,如果占比很高的话，比如超过0.9就保留，否则二次提取目标说话人的声音。我想把这个占比的结果做成反馈机制，类似监督模式，整个流程变成一个模型训练。之后直接用训练的模型去做特定说话人的人声提取。
我有个现有的代码用于从多人说话里提取目标说话人的声音(不需要完整提取，不是用来记录会议中某说话人的说话内容的，是用来RVC训练的)，现在有个问题就是提取出来的声音偶尔还会有别人的声音，大概占比10%，我想把这些也去掉。于是我想到办法，把提取出来的音频，在和目标说话人的纯净的人声embedding比对，看目标说话人在音频中的说话时间占比为,如果占比很高的话，比如超过0.9就保留，否则二次提取目标说话人的声音。我想把这个占比的结果做成反馈机制，类似监督模式，整个流程变成一个模型训练。之后直接用训练的模型去做特定说话人的人声提取。audio_processor.py里的主函数asyncdefprocess_audio(file,config_value,whisper_model,device):和defget_matched_audios(target_audio,负责的是从多人说话里提取目标说话人的声音。t3.py这里的代码是用于把提取出的人声的人声再次和目标人说话人比对，看时间占比。现在要解决把整个流程设计成模型训练的模式。...
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更新于：2025-08-25 21:42:58
|-转 python报错 can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
ERROR:app.services.audio_processor:保存音频或其他操作失败: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
ERROR:app.services.audio_processor:处理音频时发生错误: 保存音频或其他操作失败: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
ERROR:app.api.audio_router:处理音频时发生错误:
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 677, in process_audio
target_embedding = get_or_create_target_embedding(target_audio_path, CACHE_DIR, verification)
File "D:\python\fastapi-speaker-extractor\app\utils\audio_utils.py", line 110, in get_or_create_target_embedding
np.save(cache_file, embedding.numpy()) # 存为 numpy
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
During handling of the above exception, another exception occurred:
# =========================================
# 4. 函数：获取目标 embedding（支持缓存）
def get_or_create_target_embedding(audio_path, cache_dir ,verification):
os.makedirs(cache_dir, exist_ok=True)
with open(audio_path, 'rb') as f:
file_hash = hashlib.md5(f.read()).hexdigest()
cache_file = os.path.join(cache_dir, f"{file_hash}.npy")
if os.path.exists(cache_file):
print(f"✅ [缓存命中] 从缓存加载目标说话人 embedding: {cache_file}")
# 从 .npy 加载的是 numpy，需要转为 Tensor
embedding_np = np.load(cache_file)
return torch.from_numpy(embedding_np) # ✅ 转为 PyTorch Tensor
else:
print(f"🔁 [缓存未命中] 正在提取目标说话人音频的 embedding: {audio_path}")
embedding = extract_audio_embedding(audio_path ,verification) # Tensor
# 保存为 numpy 到缓存
np.save(cache_file, embedding.numpy()) # 存为 numpy
print(f"💾 [缓存保存] 已保存目标说话人 embedding 到缓存: {cache_file}")
return embedding # Tensorkimi瞬间解决了...
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更新于：2025-08-26 00:18:51
|-转 Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
根据你的问题和代码分析，这个错误是由于PyTorch张量和模型在不同设备（CPU和GPU）上导致的。我来为你提供几种解决方案：
## 问题分析
错误的核心在于：`Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!`
主要问题点：
1. 代码中存在多个模型加载点，设备管理不统一
2. 音频张量加载后未明确指定设备
3. SpeechBrain模型和PyAnnote模型可能使用了不同的设备配置
## 解决方案
### 方案一：统一设备管理（推荐）
修改代码以确保所有模型和张量都使用统一的设备配置：
[services/audio_processor.py](D:\python\fastapi-speaker-extractor\app\services/audio_processor.py)
能不能专门构建一个app/utils/device_middleware.py一个中间件，让所有项目下运行的程序统一走中间件，然后中间件统一设置device，device_middleware.py我已经创建
创建一个新的设备管理中间件，用于统一管理所有模型和张量的设备分配
[device_middleware.py](D:\python\fastapi-speaker-extractor\app\utils\device_middleware.py)
修改audio_processor.py文件，使用新的设备管理中间件
[services/audio_processor.py](D:\python\fastapi-speaker-extractor\app\services/audio_processor.py)
修改audio_utils.py文件，加入设备管理中间件的支持
[audio_utils.py](D:\python\fastapi-speaker-extractor\app\utils\audio_utils.py)
修改embedding_utils.py文件，加入设备管理中间件的支持
[embedding_utils.py](D:\python\fastapi-speaker-extractor\app\utils\embedding_utils.py)
修改model_loader.py文件，加入设备管理中间件的支持
[model_loader.py](D:\python\fastapi-speaker-extractor\app\utils\model_loader.py)
修改onnx_model_loader.py文件，加入设备管理中间件的支持
[onnx_model_loader.py](D:\python\fastapi-speaker-extractor\app\utils\onnx_model_loader.py)
修改__init__.py文件，确保设备中间件被正确导入
[__init__.py](D:\python\fastapi-speaker-extractor\app\utils\__init__.py)
修改audio_processor.py中get_matched_audios函数，确保音频处理时使用统一的设备
[services/audio_processor.py](D:\python\fastapi-speaker-extractor\app\services/audio_processor.py)
修改audio_processor.py中的process_audio函数，确保设备一致性
[services/audio_processor.py](D:\python\fastapi-speaker-extractor\app\services/audio_processor.py)
现在我已经为你实现了一个完整的设备管理解决方案，以解决你遇到的设备不一致问题。以下是我所做的更改和方案说明：
### 方案概述
我创建了一个统一的设备管理中间件（[device_middleware.py](file:///D:/python/fastapi-speaker-extractor/app/utils/device_middleware.py)），用于在整个项目中统一管理所有模型和张量的设备分配。这样可以确保所有计算都在同一设备上进行，避免了CUDA和CPU设备混用的问题。
### 主要修改内容
1. **创建设备管理中间件**：
- 新增 [app/utils/device_middleware.py](file:///D:/python/fastapi-speaker-extractor/app/utils/device_middleware.py) 文件
- 实现了 [DeviceMiddleware](file:///D:/python/fastapi-speaker-extractor/app/utils/device_middleware.py#L14-L83) 类，用于统一管理设备
- 提供了设备移动方法，确保所有张量和模型都在同一设备上
2. **修改音频处理相关文件**：
- 更新 [audio_processor.py](file:///D:/python/fastapi-speaker-extractor/app/services/audio_processor.py) 使用设备中间件
- 修改 [get_matched_audios](file:///D:/python/fastapi-speaker-extractor/app/services/audio_processor.py#L127-L302) 函数，确保处理过程中设备一致性
- 修改 [process_audio](file:///D:/python/fastapi-speaker-extractor/app/services/audio_processor.py#L719-L801) 函数，使用设备中间件的设备
3. **创建辅助工具函数**：
- 在 [audio_utils.py](file:///D:/python/fastapi-speaker-extractor/app/utils/audio_utils.py) 中添加设备管理函数
- 在 [embedding_utils.py](file:///D:/python/fastapi-speaker-extractor/app/utils/embedding_utils.py) 中添加嵌入向量设备管理函数
- 在 [model_loader.py](file:///D:/python/fastapi-speaker-extractor/app/utils/model_loader.py) 中添加模型设备管理函数
- 在 [onnx_model_loader.py](file:///D:/python/fastapi-speaker-extractor/app/utils/onnx_model_loader.py) 中添加ONNX模型设备适配
### 方案优势
1. **统一设备管理**：所有模型和张量都使用同一个设备管理器，避免了设备不一致的问题
2. **易于维护**：当需要更改设备时，只需在中间件中修改，无需更改所有相关代码
3. **兼容性好**：支持CPU、CUDA等多种设备类型...
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更新于：2025-08-26 01:31:33
|-转 腾讯元宝推荐的项目结构（音频处理项目)
fastapi-speaker-extractor/
├── app/
│ ├── api/
│ │ ├── endpoints.py # 一般的 API 路由
│ │ ├── audio_router.py # 音频处理相关的 API 路由
│ │ └── __init__.py
│ ├── services/
│ │ ├── audio_processor.py # 音频处理逻辑，如提取、相似度计算
│ │ ├── audio_dataset.py # 自定义 Dataset 类
│ │ ├── audio_model.py # PyTorch 模型定义
│ │ ├── data_loader.py # 数据加载与预处理逻辑
│ │ ├── train_model.py # 模型训练逻辑
│ │ └── __init__.py
│ ├── schemas/
│ │ ├── api_schemas.py # Pydantic 模型定义
│ │ └── __init__.py
│ ├── utils/
│ │ ├── custom_logger.py # 自定义日志记录器
│ │ ├── helpers.py # 其他辅助函数
│ │ └── __init__.py
│ ├── main.py # FastAPI 应用入口
│ ├── __init__.py
│ └── static/ # 静态文件（如需要）
├── data/
│ ├── mixed/
│ ├── target_pure/
│ ├── extracted/
│ ├── embeddings/
│ ├── labels/
│ └── models/ # 训练好的模型保存位置
├── tests/ # 测试代码
│ ├── __init__.py
│ └── test_api.py # 示例测试文件
├── requirements.txt # 项目依赖
├── README.md # 项目说明
└── .gitignore # Git 忽略规则
三、各文件夹的职责说明
- 1.app/api/
- •endpoints.py: 定义一般的 API 路由，如说话人提取的通用接口。
- •audio_router.py: 定义与音频处理相关的 API 路由，如提取目标说话人声音、语音转文字、训练模型相关接口。
- •__init__.py: 使api成为一个 Python 包。
- 2.app/services/
- •audio_processor.py: 包含音频处理的业务逻辑，如调用 SpeechBrain 进行相似度计算、音频分割、提取等。
- •audio_dataset.py: 定义自定义的 PyTorchDataset类，用于加载和处理训练数据。
- •audio_model.py: 定义 PyTorch 模型架构，如 CNN 模型。
- •data_loader.py: 包含数据加载与预处理的逻辑，如创建DataLoader、数据转换等。
- •train_model.py: 包含模型训练的逻辑，如训练循环、损失计算、优化器更新等。
- •__init__.py: 使services成为一个 Python 包。
- 3.app/schemas/
- •api_schemas.py: 定义 Pydantic 模型，用于请求和响应的数据验证与序列化。
- •__init__.py: 使schemas成为一个 Python 包。
- 4.app/utils/
- •custom_logger.py: 自定义日志记录器的实现。
- •helpers.py: 其他辅助函数，如文件操作、路径处理等。
- •__init__.py: 使utils成为一个 Python 包。
- 5.app/main.py
- •FastAPI 应用的入口文件，包含应用初始化、路由注册、事件处理等。
- 6.data/
- •存放所有与数据相关的文件，如混合音频、目标纯净音频、提取的音频、嵌入向量、标签文件和训练好的模型。
- •models/: 用于保存训练好的 PyTorch 模型。
- 7.tests/
- •存放测试代码，确保各个模块的功能正确性。
- •test_api.py: 示例测试文件，测试 API 路由。
- 8.requirements.txt
- •列出项目所需的 Python 包及其版本，便于环境部署和依赖管理。
- 9.README.md
- •项目说明文档，包括项目介绍、安装步骤、使用指南等。
- 10..gitignore
- •指定 Git 忽略的文件和目录，如虚拟环境目录、缓存文件等。
四、关于data_loader.py等模块的放置
根据上述推荐的项目结构：...
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更新于：2025-08-26 01:38:31
|-转 音频处理项目fse
安装好了anaconda软件后，先按国内的镜像源，可以用清华的镜像源，
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
设置好镜像源后，执行下面命令
conda create -n fse python=3.11
直连的用这个命令速度更快
conda create -n fse -c https://conda.anaconda.org/conda-forge --override-channels python=3.11
conda activate fse
pip3 install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu128
说明cu128就是安装的torch torchaudio torchvision对应支持的CUDA版本12.8，如果你的显卡驱动支持CUDA12.8 或更高，你都可以安装cu128，如果你显卡驱动只支持CUDA12.6那就安装
pip3 install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu126
现在测试的是cu128和cu126都可以，cu129安装了后再安装下面的其他扩展，会有些扩展安装不上
所以建议安装 torch torchaudio torchvision的cu128或cu126
pip3 install fastapi speechbrain uvicorn pydantic python-multipart librosa soundfile pydub scipy pillow python-dotenv pytest httpx onnxruntime
pip3 install pydantic_setting (如果pip安装失败尝试用conda安装)
conda install pydantic_setting (如果还是失败，网络先直连然后用conda-forge频道安装)
用这下面个命令
conda install -c https://conda.anaconda.org/conda-forge --override-channels pydantic-settings
为确保datasets和modelscope兼容，请用以下命令安装确定版本
pip install datasets==2.18.0
pip install modelscope==1.11.0
之后设置huggingface的主目录和缓存目录(不设置默认是C盘的，后面重装系统又要重新下载模型)
Win10系统下环境搭建
安装提示：此项目不要安装whisperx
ModelScope 与 numpy 2.x 冲突是官方已知 issue，
在 单一环境 里 无法同时满足 whisperx 与 ModelScope。
最稳妥：把声纹功能放到独立环境或改用 SpeechBrain。
安装好了anaconda软件后，先按国内的镜像源，可以用清华的镜像源，设置好镜像源后，执行下面命令 conda create -n fse python=3.11 直连的用这个命令速度更快 conda create -n fse -c https://conda.anaconda.org/conda-forge --override-channels python=3.11 conda activate fse pip3 install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu128 说明cu128就是安装的torch torchaudio torchvision对应支持的CUDA版本12.8，如果你的显卡驱动支持CUDA12.8 或更高，你都可以安装cu128，如果你显卡驱动只支持CUDA12.6那就安装 pip3 install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu126 现在测试的是cu128和cu126都可以，cu129安装了后再安装下面的其他扩展，会有些扩展安装不上 所以建议安装 torch torchaudio torchvision的cu128或cu126 pip3 install fastapi speechbrain uvicorn pydantic python-multipart librosa soundfile pydub scipy pillow python-dotenv pytest httpx datasets pip3 install pydantic_setting (如果pip安装失败尝试用conda安装) conda install pydantic_setting ( 直连的用这个命令 conda install -c https://conda.anaconda.org/conda-forge --override-channels pydantic-settings 之后设置huggingface的主目录和缓存目录(不设置默认是C盘的，后面重装系统又要重新下载模型)
训练代码
import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from app.services.audio_dataset import SpeakerExtractionDataset # 导入数据集类
from app.services.audio_model import SpeakerExtractionCNN1D # 导入模型类
# 参数配置
DATA_DIR = 'data/embeddings' # 数据目录
LABELS_DIR = 'data/labels' # 标签目录
EMBEDDING_DIR = 'data/embeddings' # 嵌入目录
BATCH_SIZE = 16
EPOCHS = 20
LEARNING_RATE = 0.001
MODEL_SAVE_PATH = 'models/speaker_extraction_model.pth'
EMBEDDING_DIM = 192 # 根据您的嵌入维度调整
# 设备配置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")
# 创建 Dataset 和 DataLoader
dataset = SpeakerExtractionDataset(
data_dir=DATA_DIR,
labels_dir=LABELS_DIR,
embedding_dir=EMBEDDING_DIR,
transform=None
)
dataloader = DataLoader(
dataset,
batch_size=BATCH_SIZE,
shuffle=True
)
# 初始化模型
model = SpeakerExtractionCNN1D(input_dim=1).to(device) # 根据特征维度调整 input_dim
# 损失函数和优化器
criterion = nn.MSELoss() # 回归任务，若为分类任务可改为 nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
# 训练循环
for epoch in range(EPOCHS):
model.train() # 设置模型为训练模式
running_loss = 0.0
for features, labels in tqdm(dataloader, desc=f"Epoch {epoch+1}/{EPOCHS}"):
features = features.to(device) # 将特征移动到设备
labels = labels.to(device) # 将标签移动到设备
# 前向传播
outputs = model(features)
loss = criterion(outputs, labels) # 计算损失
# 反向传播和优化
optimizer.zero_grad() # 清零梯度
loss.backward() # 反向传播
optimizer.step() # 更新参数
running_loss += loss.item() * features.size(0) # 累加损失
# 计算每个 epoch 的平均损失
epoch_loss = running_loss / len(dataloader.dataset)
print(f"Epoch {epoch+1}/{EPOCHS}, Loss: {epoch_loss:.4f}")
# 保存模型
torch.save(model.state_dict(), MODEL_SAVE_PATH)
print(f"模型已保存到 {MODEL_SAVE_PATH}")
. 代码说明
- •数据集与数据加载器： 使用SpeakerExtractionDataset类加载数据，并通过DataLoader进行批处理和打乱。
- •模型初始化： 初始化SpeakerExtractionCNN1D模型，并将其移动到相应的设备（GPU 或 CPU）。
- •损失函数与优化器： 使用均方误差损失（MSE）进行回归任务，您可以根据需要更改为分类任务的损失函数。
- •训练循环： 在每个 epoch 中，遍历数据加载器，进行前向传播、损失计算、反向传播和参数更新。使用tqdm显示训练进度。
- •模型保存： 训练完成后，将模型参数保存到指定路径。
5. 其他注意事项
- •特征与标签： 确保在SpeakerExtractionDataset中正确加载和处理特征与标签。如果使用不同的特征（如嵌入向量），请相应调整模型输入维度。
- •模型选择： 根据您的任务需求（回归或分类），选择合适的模型和损失函数。
- •超参数调整： 根据实验结果调整学习率、批次大小和训练轮数等超参数，以获得更好的模型性能。
通过上述步骤，您可以将audio_dataset.py和audio_model.py整合在一起，并编写train_audio_model.py进行模型训练。这样，您就能够利用反馈机制，训练一个模型来提高目标说话人声音提取的准确性。 ...
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更新于：2025-08-27 15:03:13
|-转 各种python 相关命令
torch.cuda.is_available()命令行查看cuda的命令
命令行（CMD / Anaconda Prompt）里直接敲：
python -c "import torch; print(torch.cuda.is_available())"
返回 True 表示 CUDA 可用，False 表示不可用。
conda create -n my -c https://conda.anaconda.org/conda-forge --override-channels mamba
用conda-forge的官方源安装 环境变量名是my的mamba环境 20250826 0712
激活环境后，没装python装python，装了python的话直接执行
pip3 install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu129
具体根据你显卡驱动的cuda版本 cu129就是cuda12.9
如果是做音频处理，建议按下面这些扩展
pip3 install fastapi uvicorn pydantic python-multipart librosa soundfile pydub scipy pillow python-dotenv pytest httpx
20250828 0720
报错
Collecting anyio<5,>=3.6.2 (from starlette<0.48.0,>=0.40.0->fastapi)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl (100 kB)
Collecting starlette<0.48.0,>=0.40.0 (from fastapi)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/f7/1f/b876b1f83aef204198a42dc101613fefccb32258e5428b5f9259677864b4/starlette-0.47.2-py3-none-any.whl (72 kB)
Collecting pydantic-core==2.33.2 (from pydantic)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a0/75/4a0a9bac998d78d889def5e4ef2b065acba8cae8c93696906c3a91f310ca/pydantic_core-2.33.2-cp313-cp313-win_amd64.whl (2.0 MB)
---------------------------------------- 2.0/2.0 MB 2.3 MB/s 0:00:00
Collecting pydantic
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6a/c0/ec2b1c8712ca690e5d61979dee872603e92b8a32f94cc1b72d53beab008a/pydantic-2.11.7-py3-none-any.whl (444 kB)
ERROR: Ignored the following versions that require a different python version: 0.52.0 Requires-Python >=3.6,<3.9; 0.52.0rc3 Requires-Python >=3.6,<3.9; 0.53.0 Requires-Python >=3.6,<3.10; 0.53.0rc1.post1 Requires-Python >=3.6,<3.10; 0.53.0rc2 Requires-Python >=3.6,<3.10; 0.53.0rc3 Requires-Python >=3.6,<3.10; 0.53.1 Requires-Python >=3.6,<3.10; 0.54.0 Requires-Python >=3.7,<3.10; 0.54.0rc2 Requires-Python >=3.7,<3.10; 0.54.0rc3 Requires-Python >=3.7,<3.10; 0.54.1 Requires-Python >=3.7,<3.10; 0.55.0 Requires-Python >=3.7,<3.11; 0.55.0rc1 Requires-Python >=3.7,<3.11; 0.55.1 Requires-Python >=3.7,<3.11; 0.55.2 Requires-Python >=3.7,<3.11; 1.10.0 Requires-Python >=3.8,<3.12; 1.10.0rc1 Requires-Python >=3.8,<3.12; 1.10.0rc2 Requires-Python >=3.8,<3.12; 1.10.1 Requires-Python >=3.8,<3.12; 1.11.0 Requires-Python >=3.9,<3.13; 1.11.0rc1 Requires-Python >=3.9,<3.13; 1.11.0rc2 Requires-Python >=3.9,<3.13; 1.11.1 Requires-Python >=3.9,<3.13; 1.11.2 Requires-Python >=3.9,<3.13; 1.11.3 Requires-Python >=3.9,<3.13; 1.21.2 Requires-Python >=3.7,<3.11; 1.21.3 Requires-Python >=3.7,<3.11; 1.21.4 Requires-Python >=3.7,<3.11; 1.21.5 Requires-Python >=3.7,<3.11; 1.21.6 Requires-Python >=3.7,<3.11; 1.26.0 Requires-Python >=3.9,<3.13; 1.26.1 Requires-Python >=3.9,<3.13; 1.6.2 Requires-Python >=3.7,<3.10; 1.6.3 Requires-Python >=3.7,<3.10; 1.7.0 Requires-Python >=3.7,<3.10; 1.7.1 Requires-Python >=3.7,<3.10; 1.7.2 Requires-Python >=3.7,<3.11; 1.7.3 Requires-Python >=3.7,<3.11; 1.8.0 Requires-Python >=3.8,<3.11; 1.8.0rc1 Requires-Python >=3.8,<3.11; 1.8.0rc2 Requires-Python >=3.8,<3.11; 1.8.0rc3 Requires-Python >=3.8,<3.11; 1.8.0rc4 Requires-Python >=3.8,<3.11; 1.8.1 Requires-Python >=3.8,<3.11; 1.9.0 Requires-Python >=3.8,<3.12; 1.9.0rc1 Requires-Python >=3.8,<3.12; 1.9.0rc2 Requires-Python >=3.8,<3.12; 1.9.0rc3 Requires-Python >=3.8,<3.12; 1.9.1 Requires-Python >=3.8,<3.12; 3.3.0 Requires-Python >=3.9,<3.13; 3.3.1 Requires-Python >=3.9,<3.13; 3.3.2 Requires-Python >=3.9,<3.13; 3.3.3 Requires-Python >=3.9,<3.13; 3.3.4 Requires-Python >=3.9,<3.13; 3.4.0 Requires-Python >=3.9,<3.13; 3.4.1 Requires-Python >=3.9,<3.13; 3.4.2 Requires-Python >=3.9,<3.13
ERROR: Could not find a version that satisfies the requirement ctranslate2==4.4.0 (from whisperx) (from versions: 4.6.0)
ERROR: No matching distribution found for ctranslate2==4.4.0
(fse) D:\python\fastapi-speaker-extractor>
(fse) D:\python\fastapi-speaker-extractor>python --version
Python 3.13.5
(fse) D:\python\fastapi-speaker-extractor>
20250826 0747...
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更新于：2025-08-26 07:48:02
|-转 python 报错 SyntaxError: 'return' outside function
{
"1b38f5a6230ac636944ef3df2a7b18a9aec530f931fed681d942267186102368": {
"short_code": "1uqjLT",
"label": 0.0,
"filepath": "outputs\\preprocessed\\1b38f5a6230ac636944ef3df2a7b18a9aec530f931fed681d942267186102368.wav",
"created_at": "2025-08-26T10:23:35.373116+08:00"
},
"0386ab79e9163b22a2b5e25a84d3443d79b5cc08d7f26d87da3bc68de8340544": {
"short_code": "1uqcmD",
"label": 0.67,
"filepath": "outputs\\preprocessed\\0386ab79e9163b22a2b5e25a84d3443d79b5cc08d7f26d87da3bc68de8340544.wav",
"created_at": "2025-08-26T03:22:45.509993+08:00"
},
"856616252eccfa58e713998fdeaaf8a2acbd896d15977bf780884ca73635cc76": {
"short_code": "1uqiqv",
"label": 0.0,
"filepath": "outputs\\preprocessed\\processed_tmpo9v0me_w_1uqiqv.wav",
"created_at": "2025-08-26T09:52:01.721592+08:00"
}
}
2025-08-26 09:14:02,637 --- app.api.audio_router --- ERROR --- 处理音频时发生错误:
"D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 75
return
^
SyntaxError: 'return' outside function
2025-08-26 09:14:02,631 --- app.api.audio_router --- WARNING --- 无法从主应用获取模型: WhisperX 模型未加载
2025-08-26 09:14:02,631 --- app.api.audio_router --- INFO --- 未提供配置参数，使用默认配置
2025-08-26 09:14:02,632 --- app.api.audio_router --- INFO --- 收到请求，文件=西游记多人16秒.wav，config={}
2025-08-26 09:14:02,637 --- app.api.audio_router --- ERROR --- 处理音频时发生错误:
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\api\audio_router.py", line 72, in extract_speaker
from app.services.audio_processor import process_audio
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 75
return
^
SyntaxError: 'return' outside function
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\api\audio_router.py", line 72, in extract_speaker
from app.services.audio_processor import process_audio
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 75
return
^
# 导入自定义模块
try:
from logger import logger
from app.utils.embedding_model_middleware import get_embedding_middleware, ModelType
from app.utils.onnx_model_loader import ONNXPyannoteEmbedding
from app.utils.error_handler import ErrorHandler
except ImportError as e:
print(f"导入工具模块失败: {e}")
return
代码 return的这个写法不知道是错哪里了。
通义灵码：
到目前为止，我们已经解决了以下几个问题：
JSON解析错误 - 通过将 config_value 参数改为可选并改进解析逻辑
类型注解兼容性问题 - 通过使用 Union[Path, str] 替代 Path | str 以支持 Python 3.8+
模型加载问题 - 通过修改模型加载逻辑确保默认加载
目录创建问题 - 通过将字符串路径转换为 Path 对象
现在您的应用程序应该能够正常处理音频文件了。
20250826 0926
G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\pydub\audio_segment.py:678: RuntimeWarning: coroutine 'UploadFile.seek' was never awaited file.seek(0) RuntimeWarning: Enable tracemalloc to get the object allocation traceback G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\pydub\audio_segment.py:678: RuntimeWarning: coroutine 'UploadFile.read' was never awaited file.seek(0) RuntimeWarning: Enable tracemalloc to get the object allocation traceback G:\ProgramData\miniconda3\envs\speaker-extractor\lib\site-packages\pydub\utils.py:268: RuntimeWarning: coroutine 'UploadFile.seek' was never awaited file.seek(0) RuntimeWarning: Enable tracemalloc to get the object allocation traceback 2025-08-26 09:26:28,638 --- fastapi-speaker-extractor --- ERROR --- ❌ 处理音频时发生错误: a bytes-like object is required, not 'coroutine' Traceback (most recent call last): File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 148, in process_audio
22222222
2025-08-26 09:29:05,415 --- fastapi-speaker-extractor --- INFO --- 📊 计算目标说话人占比 ✅ [缓存命中] 从缓存加载目标说话人 embedding: inputs/target_reference/target_reference_embedding\121e918b12035ef25024d8d9e2c8f39b.npy 🎧 正在分析音频，总长度: 3.00 秒，分段长度: 1.5 秒 2025-08-26 09:29:05,422 --- fastapi-speaker-extractor --- ERROR --- ❌ 处理音频时发生错误: 'float' object cannot be interpreted as an integer Traceback (most recent call last):
222222222222
2025-08-26 09:30:22,560 --- fastapi-speaker-extractor --- INFO --- 临时文件已清理: C:\Users\ADMINI~1\AppData\Local\Temp\tmpybyujt8p.wav
2025-08-26 09:30:22,561 --- app.api.audio_router --- ERROR --- 处理音频时发生错误:
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 188, in process_audio
proportion = calculate_target_proportion(
File "D:\python\fastapi-speaker-extractor\app\utils\audio_utils.py", line 118, in calculate_target_proportion
for i in range(num_segments):
TypeError: 'float' object cannot be interpreted as an integer
33333
raise RuntimeError(f"音频处理失败: {str(e)}")
RuntimeError: 音频处理失败: 'WindowsPath' object has no attribute 'cpu'
INFO: 127.0.0.1:14903 - "POST /api/audio/speaker/extract/ HTTP/1.1" 500 Internal Server Error
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "D:\python\fastapi-speaker-extractor\app\services\audio_processor.py", line 223, in process_audio
sha256_hash=compute_audio_sha256(output_filepath),
File "D:\python\fastapi-speaker-extractor\app\utils\common_utils.py", line 208, in compute_audio_sha256
audio_np = audio_tensor.cpu().numpy() # 转 CPU 并转为 NumPy
AttributeError: 'WindowsPath' object has no attribute 'cpu'
5555555 ...
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更新于：2025-08-26 10:37:20
|-转 python常用命令
python -m app.services.batch_process
- 打开 PowerShell（Win+X → Windows PowerShell）。
- 执行下面两条命令，一行一回车：
# 如果 profile 文件不存在，先自动创建
if (!(Test-Path $PROFILE)) { New-Item -ItemType File -Path $PROFILE -Force }
# 把激活 fse311 的语句写进去（不会重复写入）
Add-Content -Path $PROFILE -Value 'conda activate fse311'
...
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更新于：2025-08-26 11:59:52
|-转 腾讯编程助手
我已经成功修复了batch_process.py文件中的问题。主要修复包括：
- 修复了模块导入问题：添加了项目根目录到sys.path，确保Python能够找到app模块
- 移除了不存在的装饰器：删除了@gpu_memory_monitor装饰器调用
- 修复了函数参数不匹配：调整了process_single_file函数的参数
- 添加了错误处理：为文件处理和批量处理添加了try-catch块
- 添加了目录检查：检查输入目录是否存在，避免运行时错误
- 修复了类型注解：将dict改为dict[str, any]
现在batch_process.py应该能够正常运行了。主要的错误"cannot access local variable 'mixed_dialogue'"已经在audio_processor.py中修复，而batch_process.py的结构和导入问题也已经解决。 20250826 1224...
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更新于：2025-08-26 12:24:29
|-转 python一些扩展兼容安装的处理方案
这里把踩过坑记录下，目的为了节省python的扩展环境的时间，提高效率
即使有了腾讯元宝和Kimi这样的AI智能回答工具，还是要花不少时间才能找到解决方案
为确保datasets和modelscope兼容，请用以下命令安装确定版本...
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更新于：2025-08-27 15:07:21
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