目录
一、引言
二、pipeline库
2.1 概述
2.2 使用task实例化pipeline对象
2.2.1 基于task实例化“自动语音识别”
2.2.2 task列表
2.2.3 task默认模型
2.3 使用model实例化pipeline对象
2.3.1 基于model实例化“自动语音识别”
2.3.2 查看model与task的对应关系
三、总结
一、引言
pipeline（管道）是huggingface transformers库中一种极简方式使用大模型推理的抽象，将所有大模型分为语音（Audio）、计算机视觉（Computer vision）、自然语言处理（NLP）、多模态（Multimodal）等4大类，28小类任务（tasks）。共计覆盖32万个模型
本文对pipeline进行整体介绍，之后本专栏以每个task为主题，分别介绍各种task使用方法。
二、pipeline库
2.1 概述
管道是一种使用模型进行推理的简单而好用的方法。这些管道是从库中抽象出大部分复杂代码的对象，提供了专用于多项任务的简单 API，包括命名实体识别、掩码语言建模、情感分析、特征提取和问答。在使用上，主要有2种方法
- 使用task实例化pipeline对象
- 使用model实例化pipeline对象
2.2 使用task实例化pipeline对象
2.2.1 基于task实例化“自动语音识别”
自动语音识别的task为automatic-speech-recognition：
import os
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
from transformers import pipeline
speech_file = "./output_video_enhanced.mp3"
pipe = pipeline(task="automatic-speech-recognition")
result = pipe(speech_file)
print(result)2.2.2 task列表
task共计28类，按首字母排序，列表如下，直接替换2.2.1代码中的pipeline的task即可应用：
"audio-classification"：将返回一个AudioClassificationPipeline。"automatic-speech-recognition"：将返回一个AutomaticSpeechRecognitionPipeline。"depth-estimation"：将返回一个DepthEstimationPipeline。"document-question-answering"：将返回一个DocumentQuestionAnsweringPipeline。"feature-extraction"：将返回一个FeatureExtractionPipeline。"fill-mask"：将返回一个FillMaskPipeline：。"image-classification"：将返回一个ImageClassificationPipeline。"image-feature-extraction"：将返回一个ImageFeatureExtractionPipeline。"image-segmentation"：将返回一个ImageSegmentationPipeline。"image-to-image"：将返回一个ImageToImagePipeline。"image-to-text"：将返回一个ImageToTextPipeline。"mask-generation"：将返回一个MaskGenerationPipeline。"object-detection"：将返回一个ObjectDetectionPipeline。"question-answering"：将返回一个QuestionAnsweringPipeline。"summarization"：将返回一个SummarizationPipeline。"table-question-answering"：将返回一个TableQuestionAnsweringPipeline。"text2text-generation"：将返回一个Text2TextGenerationPipeline。"text-classification"("sentiment-analysis"可用别名)：将返回一个 TextClassificationPipeline。"text-generation"：将返回一个TextGenerationPipeline：。"text-to-audio"（"text-to-speech"可用别名）：将返回一个TextToAudioPipeline：。"token-classification"("ner"可用别名)：将返回一个TokenClassificationPipeline。"translation"：将返回一个TranslationPipeline。"translation_xx_to_yy"：将返回一个TranslationPipeline。"video-classification"：将返回一个VideoClassificationPipeline。"visual-question-answering"：将返回一个VisualQuestionAnsweringPipeline。"zero-shot-classification"：将返回一个ZeroShotClassificationPipeline。"zero-shot-image-classification"：将返回一个ZeroShotImageClassificationPipeline。"zero-shot-audio-classification"：将返回一个ZeroShotAudioClassificationPipeline。"zero-shot-object-detection"：将返回一个ZeroShotObjectDetectionPipeline。
2.2.3 task默认模型
针对每一个task，pipeline默认配置了模型，可以通过pipeline源代码查看：
SUPPORTED_TASKS = {
"audio-classification": {
"impl": AudioClassificationPipeline,
"tf": (),
"pt": (AutoModelForAudioClassification,) if is_torch_available() else (),
"default": {"model": {"pt": ("superb/wav2vec2-base-superb-ks", "372e048")}},
"type": "audio",
},
"automatic-speech-recognition": {
"impl": AutomaticSpeechRecognitionPipeline,
"tf": (),
"pt": (AutoModelForCTC, AutoModelForSpeechSeq2Seq) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/wav2vec2-base-960h", "55bb623")}},
"type": "multimodal",
},
"text-to-audio": {
"impl": TextToAudioPipeline,
"tf": (),
"pt": (AutoModelForTextToWaveform, AutoModelForTextToSpectrogram) if is_torch_available() else (),
"default": {"model": {"pt": ("suno/bark-small", "645cfba")}},
"type": "text",
},
"feature-extraction": {
"impl": FeatureExtractionPipeline,
"tf": (TFAutoModel,) if is_tf_available() else (),
"pt": (AutoModel,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("distilbert/distilbert-base-cased", "935ac13"),
"tf": ("distilbert/distilbert-base-cased", "935ac13"),
}
},
"type": "multimodal",
},
"text-classification": {
"impl": TextClassificationPipeline,
"tf": (TFAutoModelForSequenceClassification,) if is_tf_available() else (),
"pt": (AutoModelForSequenceClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("distilbert/distilbert-base-uncased-finetuned-sst-2-english", "af0f99b"),
"tf": ("distilbert/distilbert-base-uncased-finetuned-sst-2-english", "af0f99b"),
},
},
"type": "text",
},
"token-classification": {
"impl": TokenClassificationPipeline,
"tf": (TFAutoModelForTokenClassification,) if is_tf_available() else (),
"pt": (AutoModelForTokenClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("dbmdz/bert-large-cased-finetuned-conll03-english", "f2482bf"),
"tf": ("dbmdz/bert-large-cased-finetuned-conll03-english", "f2482bf"),
},
},
"type": "text",
},
"question-answering": {
"impl": QuestionAnsweringPipeline,
"tf": (TFAutoModelForQuestionAnswering,) if is_tf_available() else (),
"pt": (AutoModelForQuestionAnswering,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("distilbert/distilbert-base-cased-distilled-squad", "626af31"),
"tf": ("distilbert/distilbert-base-cased-distilled-squad", "626af31"),
},
},
"type": "text",
},
"table-question-answering": {
"impl": TableQuestionAnsweringPipeline,
"pt": (AutoModelForTableQuestionAnswering,) if is_torch_available() else (),
"tf": (TFAutoModelForTableQuestionAnswering,) if is_tf_available() else (),
"default": {
"model": {
"pt": ("google/tapas-base-finetuned-wtq", "69ceee2"),
"tf": ("google/tapas-base-finetuned-wtq", "69ceee2"),
},
},
"type": "text",
},
"visual-question-answering": {
"impl": VisualQuestionAnsweringPipeline,
"pt": (AutoModelForVisualQuestionAnswering,) if is_torch_available() else (),
"tf": (),
"default": {
"model": {"pt": ("dandelin/vilt-b32-finetuned-vqa", "4355f59")},
},
"type": "multimodal",
},
"document-question-answering": {
"impl": DocumentQuestionAnsweringPipeline,
"pt": (AutoModelForDocumentQuestionAnswering,) if is_torch_available() else (),
"tf": (),
"default": {
"model": {"pt": ("impira/layoutlm-document-qa", "52e01b3")},
},
"type": "multimodal",
},
"fill-mask": {
"impl": FillMaskPipeline,
"tf": (TFAutoModelForMaskedLM,) if is_tf_available() else (),
"pt": (AutoModelForMaskedLM,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("distilbert/distilroberta-base", "ec58a5b"),
"tf": ("distilbert/distilroberta-base", "ec58a5b"),
}
},
"type": "text",
},
"summarization": {
"impl": SummarizationPipeline,
"tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
"default": {
"model": {"pt": ("sshleifer/distilbart-cnn-12-6", "a4f8f3e"), "tf": ("google-t5/t5-small", "d769bba")}
},
"type": "text",
},
# This task is a special case as it's parametrized by SRC, TGT languages.
"translation": {
"impl": TranslationPipeline,
"tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
"default": {
("en", "fr"): {"model": {"pt": ("google-t5/t5-base", "686f1db"), "tf": ("google-t5/t5-base", "686f1db")}},
("en", "de"): {"model": {"pt": ("google-t5/t5-base", "686f1db"), "tf": ("google-t5/t5-base", "686f1db")}},
("en", "ro"): {"model": {"pt": ("google-t5/t5-base", "686f1db"), "tf": ("google-t5/t5-base", "686f1db")}},
},
"type": "text",
},
"text2text-generation": {
"impl": Text2TextGenerationPipeline,
"tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
"default": {"model": {"pt": ("google-t5/t5-base", "686f1db"), "tf": ("google-t5/t5-base", "686f1db")}},
"type": "text",
},
"text-generation": {
"impl": TextGenerationPipeline,
"tf": (TFAutoModelForCausalLM,) if is_tf_available() else (),
"pt": (AutoModelForCausalLM,) if is_torch_available() else (),
"default": {"model": {"pt": ("openai-community/gpt2", "6c0e608"), "tf": ("openai-community/gpt2", "6c0e608")}},
"type": "text",
},
"zero-shot-classification": {
"impl": ZeroShotClassificationPipeline,
"tf": (TFAutoModelForSequenceClassification,) if is_tf_available() else (),
"pt": (AutoModelForSequenceClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("facebook/bart-large-mnli", "c626438"),
"tf": ("FacebookAI/roberta-large-mnli", "130fb28"),
},
"config": {
"pt": ("facebook/bart-large-mnli", "c626438"),
"tf": ("FacebookAI/roberta-large-mnli", "130fb28"),
},
},
"type": "text",
},
"zero-shot-image-classification": {
"impl": ZeroShotImageClassificationPipeline,
"tf": (TFAutoModelForZeroShotImageClassification,) if is_tf_available() else (),
"pt": (AutoModelForZeroShotImageClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("openai/clip-vit-base-patch32", "f4881ba"),
"tf": ("openai/clip-vit-base-patch32", "f4881ba"),
}
},
"type": "multimodal",
},
"zero-shot-audio-classification": {
"impl": ZeroShotAudioClassificationPipeline,
"tf": (),
"pt": (AutoModel,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("laion/clap-htsat-fused", "973b6e5"),
}
},
"type": "multimodal",
},
"image-classification": {
"impl": ImageClassificationPipeline,
"tf": (TFAutoModelForImageClassification,) if is_tf_available() else (),
"pt": (AutoModelForImageClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("google/vit-base-patch16-224", "5dca96d"),
"tf": ("google/vit-base-patch16-224", "5dca96d"),
}
},
"type": "image",
},
"image-feature-extraction": {
"impl": ImageFeatureExtractionPipeline,
"tf": (TFAutoModel,) if is_tf_available() else (),
"pt": (AutoModel,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("google/vit-base-patch16-224", "3f49326"),
"tf": ("google/vit-base-patch16-224", "3f49326"),
}
},
"type": "image",
},
"image-segmentation": {
"impl": ImageSegmentationPipeline,
"tf": (),
"pt": (AutoModelForImageSegmentation, AutoModelForSemanticSegmentation) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/detr-resnet-50-panoptic", "fc15262")}},
"type": "multimodal",
},
"image-to-text": {
"impl": ImageToTextPipeline,
"tf": (TFAutoModelForVision2Seq,) if is_tf_available() else (),
"pt": (AutoModelForVision2Seq,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("ydshieh/vit-gpt2-coco-en", "65636df"),
"tf": ("ydshieh/vit-gpt2-coco-en", "65636df"),
}
},
"type": "multimodal",
},
"object-detection": {
"impl": ObjectDetectionPipeline,
"tf": (),
"pt": (AutoModelForObjectDetection,) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/detr-resnet-50", "2729413")}},
"type": "multimodal",
},
"zero-shot-object-detection": {
"impl": ZeroShotObjectDetectionPipeline,
"tf": (),
"pt": (AutoModelForZeroShotObjectDetection,) if is_torch_available() else (),
"default": {"model": {"pt": ("google/owlvit-base-patch32", "17740e1")}},
"type": "multimodal",
},
"depth-estimation": {
"impl": DepthEstimationPipeline,
"tf": (),
"pt": (AutoModelForDepthEstimation,) if is_torch_available() else (),
"default": {"model": {"pt": ("Intel/dpt-large", "e93beec")}},
"type": "image",
},
"video-classification": {
"impl": VideoClassificationPipeline,
"tf": (),
"pt": (AutoModelForVideoClassification,) if is_torch_available() else (),
"default": {"model": {"pt": ("MCG-NJU/videomae-base-finetuned-kinetics", "4800870")}},
"type": "video",
},
"mask-generation": {
"impl": MaskGenerationPipeline,
"tf": (),
"pt": (AutoModelForMaskGeneration,) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/sam-vit-huge", "997b15")}},
"type": "multimodal",
},
"image-to-image": {
"impl": ImageToImagePipeline,
"tf": (),
"pt": (AutoModelForImageToImage,) if is_torch_available() else (),
"default": {"model": {"pt": ("caidas/swin2SR-classical-sr-x2-64", "4aaedcb")}},
"type": "image",
},
}2.3 使用model实例化pipeline对象
2.3.1 基于model实例化“自动语音识别”
如果不想使用task中默认的模型，可以指定huggingface中的模型：
import os
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
from transformers import pipeline
speech_file = "./output_video_enhanced.mp3"
#transcriber = pipeline(task="automatic-speech-recognition", model="openai/whisper-medium")
pipe = pipeline(model="openai/whisper-medium")
result = pipe(speech_file)
print(result)2.3.2 查看model与task的对应关系
可以登录https://huggingface.co/tasks查看
三、总结
本文为transformers之pipeline专栏的第0篇，后面会以每个task为一篇，共计讲述28+个tasks的用法，通过28个tasks的pipeline使用学习，可以掌握语音、计算机视觉、自然语言处理、多模态乃至强化学习等30w+个huggingface上的开源大模型。让你成为大模型领域的专家！
期待您的3连+关注，如何还有时间，欢迎阅读我的其他文章：
《AI—工程篇》
AI智能体研发之路-工程篇（一）：Docker助力AI智能体开发提效
AI智能体研发之路-工程篇（二）：Dify智能体开发平台一键部署
AI智能体研发之路-工程篇（三）：大模型推理服务框架Ollama一键部署
AI智能体研发之路-工程篇（四）：大模型推理服务框架Xinference一键部署
AI智能体研发之路-工程篇（五）：大模型推理服务框架LocalAI一键部署
《AI—模型篇》
AI智能体研发之路-模型篇（一）：大模型训练框架LLaMA-Factory在国内网络环境下的安装、部署及使用
AI智能体研发之路-模型篇（二）：DeepSeek-V2-Chat 训练与推理实战
AI智能体研发之路-模型篇（三）：中文大模型开、闭源之争
AI智能体研发之路-模型篇（四）：一文入门pytorch开发
AI智能体研发之路-模型篇（五）：pytorch vs tensorflow框架DNN网络结构源码级对比
AI智能体研发之路-模型篇（六）：【机器学习】基于tensorflow实现你的第一个DNN网络
AI智能体研发之路-模型篇（七）：【机器学习】基于YOLOv10实现你的第一个视觉AI大模型
AI智能体研发之路-模型篇（八）：【机器学习】Qwen1.5-14B-Chat大模型训练与推理实战
AI智能体研发之路-模型篇（九）：【机器学习】GLM4-9B-Chat大模型/GLM-4V-9B多模态大模型概述、原理及推理实战
《AI—Transformers应用》
【AI大模型】Transformers大模型库（一）：Tokenizer
【AI大模型】Transformers大模型库（二）：AutoModelForCausalLM
【AI大模型】Transformers大模型库（三）：特殊标记（special tokens）
【AI大模型】Transformers大模型库（四）：AutoTokenizer
【AI大模型】Transformers大模型库（五）：AutoModel、Model Head及查看模型结构
