library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags:

• 从训练器生成 datasets:
• minds14 metrics:
• 准确率 model-index:
• name: my_awesome_mind_model results:
  • task: name: 音频分类 type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics:
    • name: Accuracy type: accuracy value: 0.061946902654867256

my_awesome_mind_model

该模型是 facebook/wav2vec2-base 在 minds14 数据集上的微调版本。 在评估集上取得以下结果：

• Loss: 2.6577
• Accuracy: 0.0619

模型描述

用于微调的基础模型：https://huggingface.co/facebook/wav2vec2-base

预期用途与限制

用它来熟悉如何对预训练模型进行微调。这不是一个可用于生产的模型。

训练和评估数据

这是训练数据集的链接： https://huggingface.co/datasets/PolyAI/minds14 您可以使用自己的数据，并对其进行预处理以用于您的数据训练。

训练过程

训练超参数

训练过程中使用了以下超参数：

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: 使用 OptimizerNames.ADAMW_TORCH ，参数为 betas=(0.9,0.999) 和 epsilon=1e-08，optimizer_args=无额外参数
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

训练结果

框架版本

• Transformers 4.48.2
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0

推理 - 如何使用

加载一个您想要运行推理的音频文件。如果需要，请记住将音频文件的采样率重采样到与模型匹配的采样率！

[1] 18s

Transformers 安装

! pip install transformers datasets

要从源码安装而不是最新发布版，请注释掉上面的命令并取消下面一行的注释。

! pip install git+https://github.com/huggingface/transformers.git

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Installing collected packages: xxhash, fsspec, dill, multiprocess, datasets Attempting uninstall: fsspec Found existing installation: fsspec 2024.10.0 Uninstalling fsspec-2024.10.0: Successfully uninstalled fsspec-2024.10.0 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. torch 2.5.1+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible. torch 2.5.1+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible. torch 2.5.1+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible. torch 2.5.1+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible. torch 2.5.1+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible. torch 2.5.1+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible. torch 2.5.1+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible. torch 2.5.1+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible. torch 2.5.1+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible. torch 2.5.1+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible. gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible. Successfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0 音频分类

[ ] #@title from IPython.display import HTML

HTML('')

音频分类 - 就像文本一样 - 会根据输入数据分配一个类别标签输出。唯一的区别是，这里不是文本输入，而是原始音频波形。一些实际应用包括识别说话者意图、语言分类，甚至通过声音识别动物种类。

本指南将向您展示如何：

在 MInDS-14 数据集上微调 Wav2Vec2 以分类说话者意图。 使用您的微调模型进行推理。 本教程中说明的任务受以下模型架构支持： Audio Spectrogram Transformer, Data2VecAudio, Hubert, SEW, SEW-D, UniSpeech, UniSpeechSat, Wav2Vec2, Wav2Vec2-Conformer, WavLM, Whisper

在