本文主要介绍hugging Face(拥抱脸)的简单介绍以及常见用法，用来模型测试是个好的工具~
如下图所示左边框是各项任务，包含多模态（Multimodal），计算机视觉(Computer Vision)，自然语言处理(NLP)等，右边是各任务模型。
示例1:语音识别
点击任务栏里的 Audio Classification(语音分类)任务
1.1.语音识别
点开一个模型，是基于语音的情绪识别，有的作者可能有写模型描述（Model description），任务和数据集描述（Task and dataset description）,使用示例（Usage examples）有的作者不会写的很详细。
编辑代码
import torchimport torch.nn as nnimport torch.nn.functional as Fimport torchaudiofrom transformers import AutoConfig, Wav2Vec2FeatureExtractorimport librosaimport IPython.display as ipdimport numpy as npimport pandas as pdfrom src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassificationdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model_name_or_path = "harshit345/xlsr-wav2vec-speech-emotion-recognition"config = AutoConfig.from_pretrained(model_name_or_path)feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)sampling_rate = feature_extractor.sampling_ratemodel = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)def speech_file_to_array_fn(path, sampling_rate):speech_array, _sampling_rate = torchaudio.load(path)resampler = torchaudio.transforms.Resample(_sampling_rate)speech = resampler(speech_array).squeeze().numpy()return speechdef predict(path, sampling_rate):speech = speech_file_to_array_fn(path, sampling_rate)inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)inputs = {key: inputs[key].to(device) for key in inputs}with torch.no_grad():logits = model(**inputs).logitsscores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]return outputs# path for a samplepath = '/workspace/tts_dataset/studio/studio_audio/studio_a_00000.wav'outputs = predict(path, sampling_rate)print(outputs)
更改代码中的.wav地址
要运行前必须安装的是huggingface的库，所以之前没有的话安装(建议python3.8以上)
# requirement packagespip install git+https://github.com/huggingface/datasets.gitpip install git+https://github.com/huggingface/transformers.git# 语音处理需要以下俩个库，任务不同，所安装的库不同pip install torchaudiopip install librosa
1.2.语音情绪识别
原文提供了预测方法，直接是实现不了的，需要修改
def predict_emotion_hubert(audio_file):""" inspired by an example from https://github.com/m3hrdadfi/soxan """from audio_models import HubertForSpeechClassificationfrom transformers import Wav2Vec2FeatureExtractor, AutoConfigimport torch.nn.functional as Fimport torchimport numpy as npfrom pydub import AudioSegmentmodel = HubertForSpeechClassification.from_pretrained("Rajaram1996/Hubert_emotion") # Downloading: 362Mfeature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")sampling_rate=16000 # defined by the model; must convert mp3 to this rate.config = AutoConfig.from_pretrained("Rajaram1996/Hubert_emotion")def speech_file_to_array(path, sampling_rate):# using torchaudio...# speech_array, _sampling_rate = torchaudio.load(path)# resampler = torchaudio.transforms.Resample(_sampling_rate, sampling_rate)# speech = resampler(speech_array).squeeze().numpy()sound = AudioSegment.from_file(path)sound = sound.set_frame_rate(sampling_rate)sound_array = np.array(sound.get_array_of_samples())return sound_arraysound_array = speech_file_to_array(audio_file, sampling_rate)inputs = feature_extractor(sound_array, sampling_rate=sampling_rate, return_tensors="pt", padding=True)inputs = {key: inputs[key].to("cpu").float() for key in inputs}with torch.no_grad():logits = model(**inputs).logitsscores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]outputs = [{"emo": config.id2label[i],"score": round(score * 100, 1)}for i, score in enumerate(scores)]return [row for row in sorted(outputs, key=lambda x:x["score"], reverse=True) if row['score'] != '0.0%'][:2]
也可以改为
import torchimport torch.nn as nnimport torch.nn.functional as Fimport torchaudiofrom transformers import AutoConfig, Wav2Vec2FeatureExtractorfrom src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification#dataset: AVDESSmodel_name_or_path = "Rajaram1996/Hubert_emotion"device = torch.device("cuda" if torch.cuda.is_available() else "cpu")config = AutoConfig.from_pretrained(model_name_or_path)feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)sampling_rate = feature_extractor.sampling_rate# for wav2vec#model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)# for hubertmodel = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device)def speech_file_to_array_fn(path, sampling_rate):speech_array, _sampling_rate = torchaudio.load(path)resampler = torchaudio.transforms.Resample(_sampling_rate, sampling_rate)speech = resampler(speech_array).squeeze().numpy()return speechdef predict(path, sampling_rate):speech = speech_file_to_array_fn(path, sampling_rate)inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)inputs = {key: inputs[key].to(device) for key in inputs}with torch.no_grad():logits = model(**inputs).logitsscores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score inenumerate(scores)]if outputs["Score"]return outputs# 001.wav =happath = "/workspace/dataset/emo/audio/000-001.wav"outputs = predict(path, sampling_rate)print(outputs)
示例2:多模态中的vit模型提取图片特征
from transformers import ViTFeatureExtractor, ViTModelfrom PIL import Imageimport requestsurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'image = Image.open(requests.get(url, stream=True).raw)feature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vitb16')model = ViTModel.from_pretrained('facebook/dino-vitb16')inputs = feature_extractor(images=image, return_tensors="pt")outputs = model(**inputs)last_hidden_states = outputs.last_hidden_stateprint(outputs,last_hidden_states)
运行结果
# requirement packagespip install datasetspip install transformerspip install pillow
示例3:自然语言处理（NLP）中的翻译实现
选一个 翻译任务（Translation），再选择一个中英翻译模型（一般模型名称都是模型，任务等的简写），例如下方右边的模型就表示Helsinki-NLP提供的数据集为opus,mt(machine translation机器翻译，中文翻英文)：
from transformers import AutoTokenizer, AutoModelForSeq2SeqLMtokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipelinetokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")# Elenatranslator = pipeline("translation", model=model, tokenizer=tokenizer)results = translator("我是一名研究员")print(results)
结果如图
#必须安装包pip install transformerspip install sentencepiece#建议安装pip install sacremoses
hungging face 的使用方法就到这里啦，如果想要了解相关其他内容，可私聊后台~
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