语言:
- 英语
- 阿拉伯语
数据集:
- covost2
- librispeech_asr
标签:
- 音频
- 语音翻译
- 自动语音识别
- 语音转文本2
许可证: mit
任务标签: 自动语音识别
示例:
- 示例标题: 普通语音1
来源: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
- 示例标题: 普通语音2
来源: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99987.mp3
- 示例标题: 普通语音3
来源: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99988.mp3
S2T2-Wav2Vec2-CoVoST2-EN-AR-ST
s2t-wav2vec2-large-en-ar
是一个专为端到端语音翻译(ST)训练的语音转文本Transformer模型。
该S2T2模型在论文《大规模自监督与半监督学习在语音翻译中的应用》中提出,并正式发布于Fairseq。
模型描述
S2T2是一个基于Transformer的序列到序列(语音编码器-解码器)模型,专为端到端自动语音识别(ASR)和语音翻译(ST)设计。它采用预训练的Wav2Vec2作为编码器,并搭配基于Transformer的解码器。模型通过标准的自回归交叉熵损失进行训练,并以自回归方式生成翻译结果。
用途与限制
该模型可用于端到端的英语语音到阿拉伯语文本翻译。
访问模型库以查找其他S2T2模型检查点。
使用方法
由于这是一个标准的序列到序列Transformer模型,您可以通过将语音特征传递给模型的generate
方法来生成转录文本。
您可以直接通过ASR流水线使用模型:
from datasets import load_dataset
from transformers import pipeline
librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-ar", feature_extractor="facebook/s2t-wav2vec2-large-en-ar")
translation = asr(librispeech_en[0]["file"])
或按步骤操作如下:
import torch
from transformers import Speech2Text2Processor, SpeechEncoderDecoder
from datasets import load_dataset
import soundfile as sf
model = SpeechEncoderDecoder.from_pretrained("facebook/s2t-wav2vec2-large-en-ar")
processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-ar")
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)
inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
transcription = processor.batch_decode(generated_ids)
评估结果
CoVoST-V2测试集英语-阿拉伯语翻译结果(BLEU分数):20.2
更多信息请参阅官方论文,特别是表2的第10行。
BibTeX条目与引用信息
@article{DBLP:journals/corr/abs-2104-06678,
author = {Changhan Wang and
Anne Wu and
Juan Miguel Pino and
Alexei Baevski and
Michael Auli and
Alexis Conneau},
title = {Large-Scale Self- and Semi-Supervised Learning for Speech Translation},
journal = {CoRR},
volume = {abs/2104.06678},
year = {2021},
url = {https://arxiv.org/abs/2104.06678},
archivePrefix = {arXiv},
eprint = {2104.06678},
timestamp = {Thu, 12 Aug 2021 15:37:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-06678.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}