w2v bert 2.0

提供商facebook
分类feature-extraction
许可证mit
下载量167.3K
星标0

简介

w2v-bert-2.0 是由 Meta (Facebook) 推出的预训练模型，旨在通过将 Word2Vec 的静态向量优势与 BERT 的上下文感知能力相结合，提升文本特征提取的效率。与传统的全量 BERT 不同，它更侧重于将文本转化为高质量的向量表示（Embedding），非常适合需要高性能文本表征但又不想承担大模型推理开销的开发者。对于习惯使用 Sentence-BERT 或传统词向量的用户来说，该模型提供了一个兼顾语义深度与计算速度的中间方案，上手难度低，可直接集成到现有的 NLP 检索或分类管线中。

核心亮点

  • 融合静态词向量与上下文语义，特征提取更精准
  • 推理速度快，适合大规模文本向量化场景
  • MIT 协议开源，企业级部署无版权压力
  • 完美替代传统 Word2Vec 以提升语义检索效果

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("facebook/w2v-bert-2.0")
tokenizer = AutoTokenizer.from_pretrained("facebook/w2v-bert-2.0")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download facebook/w2v-bert-2.0

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download facebook/w2v-bert-2.0 config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('facebook/w2v-bert-2.0')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/facebook/w2v-bert-2.0

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/facebook/w2v-bert-2.0

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('facebook/w2v-bert-2.0')
tokenizer = AutoTokenizer.from_pretrained('facebook/w2v-bert-2.0')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model facebook/w2v-bert-2.0

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model facebook/w2v-bert-2.0 README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('facebook/w2v-bert-2.0')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/facebook/w2v-bert-2.0.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/facebook/w2v-bert-2.0.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'facebook/w2v-bert-2.0')

完整文档

来源: HuggingFace

---
license: mit
language:

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inference: false
---

W2v-BERT 2.0 speech encoder

We are open-sourcing our Conformer-based W2v-BERT 2.0 speech encoder as described in Section 3.2.1 of the paper, which is at the core of our Seamless models.

This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification.

| Model Name | #params | checkpoint |
| ----------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| W2v-BERT 2.0 | 600M | checkpoint

This model and its training are supported by 🤗 Transformers, more on it in the docs.

🤗 Transformers usage

This is a bare checkpoint without any modeling head, and thus requires finetuning to be used for downstream tasks such as ASR. You can however use it to extract audio embeddings from the top layer with this code snippet:

python
from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
import torch
from datasets import load_dataset

dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate


processor = AutoProcessor.from_pretrained("facebook/w2v-bert-2.0")
model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")


audio file is decoded on the fly

inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
    outputs = model(inputs)

To learn more about the model use, refer to the following resources:



Seamless Communication usage

This model can be used in Seamless Communication, where it was released.

Here's how to make a forward pass through the voice encoder, after having completed the installation steps:

python
import torch

from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
from fairseq2.memory import MemoryBlock
from fairseq2.nn.padding import get_seqs_and_padding_mask
from pathlib import Path
from seamless_communication.models.conformer_shaw import load_conformer_shaw_model


audio_wav_path, device, dtype = ...
audio_decoder = AudioDecoder(dtype=torch.float32, device=device)
fbank_converter = WaveformToFbankConverter(
    num_mel_bins=80,
    waveform_scale=215,
    channel_last=True,
    standardize=True,
    device=device,
    dtype=dtype,
)
collater = Collater(pad_value=1)


model = load_conformer_shaw_model("conformer_shaw", device=device, dtype=dtype)
model.eval()


with Path(audio_wav_path).open("rb") as fb:
    block = MemoryBlock(fb.read())


decoded_audio = audio_decoder(block)
src = collater(fbank_converter(decoded_audio))["fbank"]
seqs, padding_mask = get_seqs_and_padding_mask(src)


with torch.inference_mode():
  seqs, padding_mask = model.encoder_frontend(seqs, padding_mask)
  seqs, padding_mask = model.encoder(seqs, padding_mask)

查看官方来源