实用篇 | huggingface的一些应用指导

 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。

本文测试主要有

目录

示例1:语音识别

1.1.语音识别

1.2.语音情绪识别

示例2:多模态中的vit模型提取图片特征

示例3:自然语言处理（NLP）中的翻译实现

问题与解决

---

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。

 虽然是不同用户的模型，但是实现都是语音情绪识别，相同hubert模型，训练的数据集不同~

 使用案例

git clone https://github.com/m3hrdadfi/soxan.git

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification
 
device = 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_rate
model = 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 speech
def 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).logits
    scores = 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 sample
path = '/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 packages
pip install git+https://github.com/huggingface/datasets.git
pip 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 torchaudio
pip 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 HubertForSpeechClassification
    from transformers import  Wav2Vec2FeatureExtractor, AutoConfig
    import torch.nn.functional as F
    import torch
    import numpy as np
    from pydub import AudioSegment
 
    model = HubertForSpeechClassification.from_pretrained("Rajaram1996/Hubert_emotion") # Downloading: 362M
    feature_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_array
 
    sound_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).logits
 
    scores = 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]

 当然可以不按作者的写

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification
 
#dataset: AVDESS
model_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 hubert
model = 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 speech
 
 
def 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).logits
 
    scores = 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)]
               if outputs["Score"]
    return outputs
 
# 001.wav =hap
 
path = "/workspace/dataset/emo/audio/000-001.wav"
outputs = predict(path, sampling_rate)   
 
print(outputs)
 

1.3.语音合成

在huggingface上新建一个空间(Space),

上传文件，并运行

如果出错请参考【】

示例2:多模态中的vit模型提取图片特征

 点进模型后，复制代码（如果想查看输出，还需要自己去进行print）

from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
 
url = '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_state
 
print(outputs,last_hidden_states)

结果

代码会下载其中的文件和打包好的模型，也就是下图这些文件

*注意：这里要求库,安装命令如下

# requirement packages
datasets
transformers
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, AutoModelForSeq2SeqLM
 
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
 
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")

 提供的代码只是如何下载模型的代码，如果还要简单测试的话，需要自己编写代码进行测试

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
 
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
 
# Elena
translator = pipeline("translation", model=model, tokenizer=tokenizer)
results = translator("我是一名研究员")
print(results)

 结果如图

 *注意：这里要求库,安装命令如下

#必须安装包
pip install transformers
pip install sentencepiece
 
#建议安装
pip install sacremoses

问题与解决

【PS1】windows系统，anaconda虚拟环境中，pip安装l出错

 Could not build wheels for soxr which use PEP 517 and cannot be installed directly

python -m pip install --upgrade pip
python -m pip install --upgrade setuptools

 更新后如图

 ERROR: Could not build wheels for soxr, which is required to install pyproject.toml-based projects

安装对应的whl文件

下载并安装对应的whl文件，可以通过以下地址下载。
Unofficial Windows Binaries for Python Extension Packages

获得whl文件后，直接 pip install 【whl文件绝对路径】
然后就可以装成功了。

【PS2】运行app.py后出现Build error Build failed with exit code: 1

参考Receiving this error even with a new space - Spaces - Hugging Face Forums 

修改README.md,将python版本改为3.9.13

如果依旧出现

 ERROR: process "/bin/sh -c pip install --no-cache-dir -r requirements.txt" did not complete successfully: exit code: 1

 那么参考python - Getting "The command '/bin/sh -c pip install --no-cache-dir -r requirements.txt' returned a non-zero code: 1" while building an image from dockerfile - Stack Overflow

需要设置

• libpq-dev
• gcc