dataset=load_dataset("ashraq/esc50")#{'filename':'1-100210-B-36.wav','fold':1,'target':36,'category':'vacuum_cleaner','esc10':False,'src_file':100210,'take':'B','audio':{'path':None,'array':array([0.53897095,0.39627075,0.26739502,...,0.09729004,0.11227417,0.07983398]),'sampling_...
pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(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 今天介绍Audio音频的第四篇,零样本音频分类(zero-shot-audio-classification),在huggin...
Here, we can find additional information about the dataset, see what models are trained on the dataset and, most excitingly, listen to actual audio samples. The Dataset Preview is presented in the middle of the dataset card. It shows us the first 100 samples for each subset and split. What...
# 数据预处理参数preprocess_conf:# 是否使用HF上的Wav2Vec2类似模型提取音频特征use_hf_model:False# 音频预处理方法,也可以叫特征提取方法# 当use_hf_model为False时,支持:MelSpectrogram、Spectrogram、MFCC、Fbank# 当use_hf_model为True时,指定的是HuggingFace的模型或者本地路径,比如facebook/w2v-bert-2.0或者...
Meta 已允许该模型的商业使用,并在 Huggingface 上发布了一个供演示用的网页应用。 延伸阅读 谷歌推出 MusicLM,从文本生成音乐的模型 </code>
Model Zoo:modelscope,huggingface Online Demo:modelscope demo,huggingface space Highlights 🎯 SenseVoice focuses on high-accuracy multilingual speech recognition, speech emotion recognition, and audio event detection. Multilingual Speech Recognition: Trained with over 400,000 hours of data, supporting more...
from datasets import load_dataset common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True) print(next(iter(common_voice_es)))--- RuntimeError Traceback (most recent call last) Cell In[4], line 2 1 common_voice_es = load_dataset("common_voice", "...
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#ai #语音 【AI丁真】在线语音生成(GPT-SoVITS)模型作者:Xz乔希 https://space.bilibili.com/5859321 【GPT-SoVITS】在线合集:https://www.modelscope.cn - 东乡系统于20240214发布在抖音,已经收获了4025个喜欢,来抖音,记录美好生活!
GitHub - felixchenfy/Speech-Commands-Classification-by-LSTM-PyTorch: Classification of 11 types of audio clips using MFCCs features and LSTM. Pretrained on Speech Command Dataset with intensive data augmentation. https://arxiv.org/pdf/1610.00087.pdf ...